Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

ABSTRACT

Disclosed herein is a method of transmitting point cloud data. The method may include acquiring point cloud data, encoding geometry information including positions of points in the point cloud data, encoding attribute information including attribute values of points in the point cloud data based on the geometry information, and transmitting the encoded geometry information, the encoded attribute information and signaling information.

This application is a Continuation of U.S. patent application Ser. No.17/062,991, filed Oct. 5, 2020, which claims the benefit of earlierfiling date and right of priority to U.S. Provisional Applications No.62/910,398 filed on Oct. 3, 2019, the contents of which are herebyincorporated by reference herein in their entirety.

BACKGROUND OF THE INVENTION Field of the Invention

Embodiments relate to a method and apparatus for processing point cloudcontent.

Discussion of the Related Art

Point cloud content is content represented by a point cloud, which is aset of points belonging to a coordinate system representing athree-dimensional space. The point cloud content may express mediaconfigured in three dimensions, and is used to provide various servicessuch as virtual reality (VR), augmented reality (AR), mixed reality(MR), XR (Extended Reality), and self-driving services. However, tens ofthousands to hundreds of thousands of point data are required torepresent point cloud content. Therefore, there is a need for a methodfor efficiently processing a large amount of point data.

SUMMARY OF THE INVENTION

An object of the present disclosure devised to solve the above-describedproblems is to provide a point cloud data transmission device, a pointcloud data transmission method, a point cloud data reception device, anda point cloud data reception method for efficiently transmitting andreceiving a point cloud

Another object of the present disclosure is to provide a point clouddata transmission device, a point cloud data transmission method, apoint cloud data reception device, and a point cloud data receptionmethod for addressing latency and encoding/decoding complexity

Another object of the present disclosure is to provide a point clouddata transmission device, a point cloud data transmission method, apoint cloud data reception device, and a point cloud data receptionmethod for improving the compression performance of the point cloud byimproving the technique of encoding attribute information ofgeometry-based point cloud compression (G-PCC).

Another object of the present disclosure is to provide a point clouddata transmission device, a point cloud data transmission method, apoint cloud data reception device, and a point cloud data receptionmethod for enhancing compression efficiency while supporting parallelprocessing of attribute information of G-PCC.

Another object of the present disclosure is to provide a point clouddata transmission device, a point cloud data transmission method, apoint cloud data reception device, and a point cloud data receptionmethod for reducing the size of an attribute bitstream and enhancingcompression efficiency of attributes by proposing a method of selectingan appropriate predictor candidate among predictor candidates inencoding attribute information of G-PCC.

Objects of the present disclosure are not limited to the aforementionedobjects, and other objects of the present disclosure which are notmentioned above will become apparent to those having ordinary skill inthe art upon examination of the following description.

To achieve these objects and other advantages and in accordance with thepurpose of the disclosure, as embodied and broadly described herein, amethod of transmitting point cloud data may include acquiring pointcloud data, encoding geometry information including positions of pointsin the point cloud data, encoding attribute information includingattribute values of points in the point cloud data based on the geometryinformation, and transmitting the encoded geometry information, theencoded attribute information and signaling information.

According to embodiments, encoding attribute information comprisesgenerating predictor candidates based on nearest neighbor points of apoint to be encoded, obtaining a score of each predictor candidate andsetting a prediction mode corresponding a predictor candidate that has asmallest score to a prediction mode of the point, and encoding theprediction mode of the point and a residual attribute value of the pointobtained based on the prediction mode of the point.

According to embodiments, a maximum difference value between attributevalues of the nearest neighbor points is equal or greater than athreshold value.

According to embodiments, encoding attribute information sets one of aprediction mode 1, a prediction mode 2, or a prediction mode 3 to theprediction mode of the point, a predicted attribute value of the pointin the prediction mode 1 is determined based on an attribute value of afirst nearest neighbor point of the nearest neighbor points, thepredicted attribute value of the point in the prediction mode 2 isdetermined based on an attribute value of a second nearest neighborpoint of the nearest neighbor points, and the predicted attribute valueof the point in the prediction mode 3 is determined based on anattribute value of a third nearest neighbor point of the nearestneighbor points.

According to embodiments, encoding attribute information obtains thescore of each predictor candidate based on each residual attribute valueobtained by applying each of the prediction mode 1, the prediction mode2, and the prediction mode 3.

According to embodiments, encoding attribute information furthercomprises setting a pre-defined prediction mode to the prediction modeof the point when the maximum difference value between attribute valuesof the nearest neighbor points is less than the threshold value, andencoding a residual attribute value obtained based on the set predictionmode.

According to embodiments, the pre-defined prediction mode is aprediction mode 0, and a predicted attribute value of the point in theprediction mode 0 is determined by a distance based weighted averagevalue of the nearest neighbor points.

According to embodiments, a point cloud data transmission apparatus mayinclude an acquisition unit to acquire point cloud data, a geometryencoder to encode geometry information including positions of points inthe point cloud data, an attribute encoder to encode attributeinformation including attribute values of points in the point cloud databased on the geometry information, and a transmitter to transmit theencoded geometry information, the encoded attribute information andsignaling information.

According to embodiments, the attribute encoder generates predictorcandidates based on nearest neighbor points of a point to be encoded,obtains a score of each predictor candidate, sets a prediction modecorresponding a predictor candidate that has a smallest score to aprediction mode of the point, and encode the prediction mode of thepoint and a residual attribute value of the point obtained based on theprediction mode of the point.

According to embodiments, a maximum difference value between attributevalues of the nearest neighbor points is equal or greater than athreshold value.

According to embodiments, the attribute encoder sets one of a predictionmode 1, a prediction mode 2, or a prediction mode 3 to the predictionmode of the point, a predicted attribute value of the point in theprediction mode 1 is determined based on an attribute value of a firstnearest neighbor point of the nearest neighbor points, the predictedattribute value of the point in the prediction mode 2 is determinedbased on an attribute value of a second nearest neighbor point of thenearest neighbor points, and the predicted attribute value of the pointin the prediction mode 3 is determined based on an attribute value of athird nearest neighbor point of the nearest neighbor points.

According to embodiments, the attribute encoder obtains the score ofeach predictor candidate based on each residual attribute value obtainedby applying each of the prediction mode 1, the prediction mode 2, andthe prediction mode 3.

According to embodiments, the attribute encoder further sets apre-defined prediction mode to the prediction mode of the point when themaximum difference value between attribute values of the nearestneighbor points is less than the threshold value, and encodes a residualattribute value obtained based on the set prediction mode.

According to embodiments, the pre-defined prediction mode is aprediction mode 0, and a predicted attribute value of the point in theprediction mode 0 is determined by a distance based weighted averagevalue of the nearest neighbor points.

According to embodiments, a point cloud data reception apparatus mayinclude a receiver to receive geometry information, attributeinformation, and signaling information, a geometry decoder to decode thegeometry information based on the signaling information and restorepositions of points, an attribute decoder to decode the attributeinformation based on the signaling information and the geometryinformation and restore attribute values of the points, and a rendererto render point cloud data restored based on the positions and attributevalues of the points.

According to embodiments, the attribute decoder obtains a predictionmode of a point to be decoded, obtains a predicted attribute value ofthe point based on the prediction mode of the point, and restores anattribute value of the point based on the predicted attribute value ofthe point and a residual attribute value of the point in the attributeinformation.

According to embodiments, the attribute decoder obtains the predictionmode of the point from the attribute information when a maximumdifference value between attribute values of nearest neighbor points ofthe point is equal or greater than a threshold value, and the obtainedprediction mode of the point is one of a prediction mode 1, a predictionmode 2 or a prediction mode 3.

According to embodiments, the predicted attribute value of the point inthe prediction mode 1 is determined based on an attribute value of afirst nearest neighbor point of the nearest neighbor points, thepredicted attribute value of the point in the prediction mode 2 isdetermined based on an attribute value of a second nearest neighborpoint of the nearest neighbor points, and the predicted attribute valueof the point in the prediction mode 3 is determined based on anattribute value of a third nearest neighbor point of the nearestneighbor points.

According to embodiments, the prediction mode of the point is apre-defined prediction mode when the maximum difference value betweenattribute values of the nearest neighbor points is less than thethreshold value, and encodes a residual attribute value obtained basedon the set prediction mode.

According to embodiments, the pre-defined prediction mode is aprediction mode 0, and the predicted attribute value of the point in theprediction mode 0 is determined by a distance based weighted averagevalue of the nearest neighbor points.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosure and are incorporated in and constitute apart of this application, illustrate embodiment(s) of the disclosure andtogether with the description serve to explain the principle of thedisclosure. In the drawings:

FIG. 1 illustrates an exemplary point cloud content providing systemaccording to embodiments.

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments.

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments.

FIG. 4 illustrates an exemplary block diagram of point cloud videoencoder according to embodiments.

FIG. 5 illustrates an example of voxels in a 3D space according toembodiments.

FIG. 6 illustrates an example of octree and occupancy code according toembodiments.

FIG. 7 illustrates an example of a neighbor node pattern according toembodiments.

FIG. 8 illustrates an example of point configuration of a point cloudcontent for each LOD according to embodiments.

FIG. 9 illustrates an example of point configuration of a point cloudcontent for each LOD according to embodiments.

FIG. 10 illustrates an example of a block diagram of a point cloud videodecoder according to embodiments.

FIG. 11 illustrates an example of a point cloud video decoder accordingto embodiments.

FIG. 12 illustrates a configuration for point cloud video encoding of atransmission device according to embodiments.

FIG. 13 illustrates a configuration for point cloud video decoding of areception device according to embodiments.

FIG. 14 illustrates an exemplary structure operatively connectable witha method/device for transmitting and receiving point cloud dataaccording to embodiments.

FIG. 15 illustrates an example of a point cloud transmission deviceaccording to embodiments.

FIG. 16(a) to FIG. 16(c) illustrate an embodiment of partitioning abounding box into one or more tiles.

FIG. 17 illustrates an example of a geometry encoder and an attributeencoder according to embodiments.

FIG. 18 is a flowchart of a method of setting a prediction modeaccording to an embodiment.

FIG. 19 is a flowchart of a method of setting a prediction modeaccording to another embodiment.

FIG. 20 illustrates an example of a point cloud reception deviceaccording to embodiments.

FIG. 21 illustrates an example of a geometry decoder and an attributedecoder according to embodiments.

FIG. 22 illustrates an exemplary bitstream structure for point clouddata for transmission/reception according to embodiments.

FIG. 23 illustrates an exemplary bitstream structure for point clouddata according to embodiments.

FIG. 24 illustrates a connection relationship between components in abitstream of point cloud data according to embodiments.

FIG. 25 illustrates an embodiment of a syntax structure of a sequenceparameter set according to embodiments.

FIG. 26 illustrates an embodiment of a syntax structure of a geometryparameter set according to embodiments.

FIG. 27 illustrates an embodiment of a syntax structure of an attributeparameter set according to embodiments.

FIG. 28 illustrates an embodiment of a syntax structure of a tileparameter set according to embodiments.

FIG. 29 illustrates an embodiment of a syntax structure of geometryslice bitstream( ) according to embodiments.

FIG. 30 illustrates an embodiment of a syntax structure of geometryslice header according to embodiments.

FIG. 31 illustrates an embodiment of a syntax structure of geometryslice data according to embodiments.

FIG. 32 illustrates an embodiment of a syntax structure of attributeslice bitstream( ) according to embodiments.

FIG. 33 illustrates an embodiment of a syntax structure of attributeslice header according to embodiments.

FIG. 34 illustrates an embodiment of a syntax structure of attributeslice data according to embodiments.

FIG. 35 is a flowchart of a method of transmitting point cloud dataaccording to embodiments.

FIG. 36 is a flowchart of a method of receiving point cloud dataaccording to embodiments.

DETAILED DESCRIPTION OF THE INVENTION

Description will now be given in detail according to exemplaryembodiments disclosed herein, with reference to the accompanyingdrawings. For the sake of brief description with reference to thedrawings, the same or equivalent components may be provided with thesame reference numbers, and description thereof will not be repeated. Itshould be noted that the following examples are only for embodying thepresent disclosure and do not limit the scope of the present disclosure.What can be easily inferred by an expert in the technical field to whichthe present invention belongs from the detailed description and examplesof the present disclosure is to be interpreted as being within the scopeof the present disclosure.

The detailed description in this present specification should beconstrued in all aspects as illustrative and not restrictive. The scopeof the disclosure should be determined by the appended claims and theirlegal equivalents, and all changes coming within the meaning andequivalency range of the appended claims are intended to be embracedtherein.

Reference will now be made in detail to the preferred embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. The detailed description, which will be givenbelow with reference to the accompanying drawings, is intended toexplain exemplary embodiments of the present disclosure, rather than toshow the only embodiments that can be implemented according to thepresent disclosure. The following detailed description includes specificdetails in order to provide a thorough understanding of the presentdisclosure. However, it will be apparent to those skilled in the artthat the present disclosure may be practiced without such specificdetails. Although most terms used in this specification have beenselected from general ones widely used in the art, some terms have beenarbitrarily selected by the applicant and their meanings are explainedin detail in the following description as needed. Thus, the presentdisclosure should be understood based upon the intended meanings of theterms rather than their simple names or meanings. In addition, thefollowing drawings and detailed description should not be construed asbeing limited to the specifically described embodiments, but should beconstrued as including equivalents or substitutes of the embodimentsdescribed in the drawings and detailed description.

FIG. 1 shows an exemplary point cloud content providing system accordingto embodiments.

The point cloud content providing system illustrated in FIG. 1 mayinclude a transmission device 10000 and a reception device 10004. Thetransmission device 10000 and the reception device 10004 are capable ofwired or wireless communication to transmit and receive point clouddata.

The point cloud data transmission device 10000 according to theembodiments may secure and process point cloud video (or point cloudcontent) and transmit the same. According to embodiments, thetransmission device 10000 may include a fixed station, a basetransceiver system (BTS), a network, an artificial intelligence (AI)device and/or system, a robot, an AR/VR/XR device and/or server.According to embodiments, the transmission device 10000 may include adevice, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Thing (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The transmission device 10000 according to the embodiments includes apoint cloud video acquisition unit 10001, a point cloud video encoder10002, and/or a transmitter (or communication module) 10003.

The point cloud video acquisition unit 10001 according to theembodiments acquires a point cloud video through a processing processsuch as capture, synthesis, or generation. The point cloud video ispoint cloud content represented by a point cloud, which is a set ofpoints positioned in a 3D space, and may be referred to as point cloudvideo data. The point cloud video according to the embodiments mayinclude one or more frames. One frame represents a still image/picture.Therefore, the point cloud video may include a point cloudimage/frame/picture, and may be referred to as a point cloud image,frame, or picture.

The point cloud video encoder 10002 according to the embodiments encodesthe acquired point cloud video data. The point cloud video encoder 10002may encode the point cloud video data based on point cloud compressioncoding. The point cloud compression coding according to the embodimentsmay include geometry-based point cloud compression (G-PCC) coding and/orvideo-based point cloud compression (V-PCC) coding or next-generationcoding. The point cloud compression coding according to the embodimentsis not limited to the above-described embodiment. The point cloud videoencoder 10002 may output a bitstream containing the encoded point cloudvideo data. The bitstream may contain not only the encoded point cloudvideo data, but also signaling information related to encoding of thepoint cloud video data.

The transmitter 10003 according to the embodiments transmits thebitstream containing the encoded point cloud video data. The bitstreamaccording to the embodiments is encapsulated in a file or segment (forexample, a streaming segment), and is transmitted over various networkssuch as a broadcasting network and/or a broadband network. Although notshown in the figure, the transmission device 10000 may include anencapsulator (or an encapsulation module) configured to perform anencapsulation operation. According to embodiments, the encapsulator maybe included in the transmitter 10003. According to embodiments, the fileor segment may be transmitted to the reception device 10004 over anetwork, or stored in a digital storage medium (e.g., USB, SD, CD, DVD,Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to theembodiments is capable of wired/wireless communication with thereception device 10004 (or the receiver 10005) over a network of 4G, 5G,6G, etc. In addition, the transmitter may perform a necessary dataprocessing operation according to the network system (e.g., a 4G, 5G or6G communication network system). The transmission device 10000 maytransmit the encapsulated data in an on-demand manner.

The reception device 10004 according to the embodiments includes areceiver 10005, a point cloud video decoder 10006, and/or a renderer10007. According to embodiments, the reception device 10004 may includea device, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Things (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The receiver 10005 according to the embodiments receives the bitstreamcontaining the point cloud video data or the file/segment in which thebitstream is encapsulated from the network or storage medium. Thereceiver 10005 may perform necessary data processing according to thenetwork system (for example, a communication network system of 4G, 5G,6G, etc.). The receiver 10005 according to the embodiments maydecapsulate the received file/segment and output a bitstream. Accordingto embodiments, the receiver 10005 may include a decapsulator (or adecapsulation module) configured to perform a decapsulation operation.The decapsulator may be implemented as an element (or component ormodule) separate from the receiver 10005.

The point cloud video decoder 10006 decodes the bitstream containing thepoint cloud video data. The point cloud video decoder 10006 may decodethe point cloud video data according to the method by which the pointcloud video data is encoded (for example, in a reverse process of theoperation of the point cloud video encoder 10002). Accordingly, thepoint cloud video decoder 10006 may decode the point cloud video data byperforming point cloud decompression coding, which is the inverseprocess of the point cloud compression. The point cloud decompressioncoding includes G-PCC coding.

The renderer 10007 renders the decoded point cloud video data. Therenderer 10007 may output point cloud content by rendering not only thepoint cloud video data but also audio data. According to embodiments,the renderer 10007 may include a display configured to display the pointcloud content. According to embodiments, the display may be implementedas a separate device or component rather than being included in therenderer 10007.

The arrows indicated by dotted lines in the drawing represent atransmission path of feedback information acquired by the receptiondevice 10004. The feedback information is information for reflectinginteractivity with a user who consumes the point cloud content, andincludes information about the user (e.g., head orientation information,viewport information, and the like). In particular, when the point cloudcontent is content for a service (e.g., self-driving service, etc.) thatrequires interaction with the user, the feedback information may beprovided to the content transmitting side (e.g., the transmission device10000) and/or the service provider. According to embodiments, thefeedback information may be used in the reception device 10004 as wellas the transmission device 10000, or may not be provided.

The head orientation information according to embodiments is informationabout the user's head position, orientation, angle, motion, and thelike. The reception device 10004 according to the embodiments maycalculate the viewport information based on the head orientationinformation. The viewport information may be information about a regionof a point cloud video that the user is viewing. A viewpoint is a pointthrough which the user is viewing the point cloud video, and may referto a center point of the viewport region. That is, the viewport is aregion centered on the viewpoint, and the size and shape of the regionmay be determined by a field of view (FOV). Accordingly, the receptiondevice 10004 may extract the viewport information based on a vertical orhorizontal FOV supported by the device in addition to the headorientation information. Also, the reception device 10004 performs gazeanalysis or the like to check the way the user consumes a point cloud, aregion that the user gazes at in the point cloud video, a gaze time, andthe like. According to embodiments, the reception device 10004 maytransmit feedback information including the result of the gaze analysisto the transmission device 10000. The feedback information according tothe embodiments may be acquired in the rendering and/or display process.The feedback information according to the embodiments may be secured byone or more sensors included in the reception device 10004. According toembodiments, the feedback information may be secured by the renderer10007 or a separate external element (or device, component, or thelike).

The dotted lines in FIG. 1 represent a process of transmitting thefeedback information secured by the renderer 10007. The point cloudcontent providing system may process (encode/decode) point cloud databased on the feedback information. Accordingly, the point cloud videodecoder 10006 may perform a decoding operation based on the feedbackinformation. The reception device 10004 may transmit the feedbackinformation to the transmission device 10000. The transmission device10000 (or the point cloud video encoder 10002) may perform an encodingoperation based on the feedback information. Accordingly, the pointcloud content providing system may efficiently process necessary data(e.g., point cloud data corresponding to the user's head position) basedon the feedback information rather than processing (encoding/decoding)the entire point cloud data, and provide point cloud content to theuser.

According to embodiments, the transmission device 10000 may be called anencoder, a transmitting device, a transmitter, a transmission system, orthe like, and the reception device 10004 may be called a decoder, areceiving device, a receiver, a reception system, or the like.

The point cloud data processed in the point cloud content providingsystem of FIG. 1 according to embodiments (through a series of processesof acquisition/encoding/transmission/decoding/rendering) may be referredto as point cloud content data or point cloud video data. According toembodiments, the point cloud content data may be used as a conceptcovering metadata or signaling information related to the point clouddata.

The elements of the point cloud content providing system illustrated inFIG. 1 may be implemented by hardware, software, a processor, and/or acombination thereof.

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments.

The block diagram of FIG. 2 shows the operation of the point cloudcontent providing system described in FIG. 1 . As described above, thepoint cloud content providing system may process point cloud data basedon point cloud compression coding (e.g., G-PCC).

The point cloud content providing system according to the embodiments(for example, the point cloud transmission device 10000 or the pointcloud video acquisition unit 10001) may acquire a point cloud video(20000). The point cloud video is represented by a point cloud belongingto a coordinate system for expressing a 3D space. The point cloud videoaccording to the embodiments may include a Ply (Polygon File format orthe Stanford Triangle format) file. When the point cloud video has oneor more frames, the acquired point cloud video may include one or morePly files. The Ply files contain point cloud data, such as pointgeometry and/or attributes. The geometry includes positions of points.The position of each point may be represented by parameters (forexample, values of the X, Y, and Z axes) representing athree-dimensional coordinate system (e.g., a coordinate system composedof X, Y and Z axes). The attributes include attributes of points (e.g.,information about texture, color (in YCbCr or RGB), reflectance r,transparency, etc. of each point). A point has one or more attributes.For example, a point may have an attribute that is a color, or twoattributes that are color and reflectance.

According to embodiments, the geometry may be called positions, geometryinformation, geometry data, or the like, and the attribute may be calledattributes, attribute information, attribute data, or the like.

The point cloud content providing system (for example, the point cloudtransmission device 10000 or the point cloud video acquisition unit10001) may secure point cloud data from information (e.g., depthinformation, color information, etc.) related to the acquisition processof the point cloud video.

The point cloud content providing system (for example, the transmissiondevice 10000 or the point cloud video encoder 10002) according to theembodiments may encode the point cloud data (20001). The point cloudcontent providing system may encode the point cloud data based on pointcloud compression coding. As described above, the point cloud data mayinclude the geometry and attributes of a point. Accordingly, the pointcloud content providing system may perform geometry encoding of encodingthe geometry and output a geometry bitstream. The point cloud contentproviding system may perform attribute encoding of encoding attributesand output an attribute bitstream. According to embodiments, the pointcloud content providing system may perform the attribute encoding basedon the geometry encoding. The geometry bitstream and the attributebitstream according to the embodiments may be multiplexed and output asone bitstream. The bitstream according to the embodiments may furthercontain signaling information related to the geometry encoding andattribute encoding.

The point cloud content providing system (for example, the transmissiondevice 10000 or the transmitter 10003) according to the embodiments maytransmit the encoded point cloud data (20002). As illustrated in FIG. 1, the encoded point cloud data may be represented by a geometrybitstream and an attribute bitstream. In addition, the encoded pointcloud data may be transmitted in the form of a bitstream together withsignaling information related to encoding of the point cloud data (forexample, signaling information related to the geometry encoding and theattribute encoding). The point cloud content providing system mayencapsulate a bitstream that carries the encoded point cloud data andtransmit the same in the form of a file or segment.

The point cloud content providing system (for example, the receptiondevice 10004 or the receiver 10005) according to the embodiments mayreceive the bitstream containing the encoded point cloud data. Inaddition, the point cloud content providing system (for example, thereception device 10004 or the receiver 10005) may demultiplex thebitstream.

The point cloud content providing system (e.g., the reception device10004 or the point cloud video decoder 10005) may decode the encodedpoint cloud data (e.g., the geometry bitstream, the attribute bitstream)transmitted in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the point cloud video data based on thesignaling information related to encoding of the point cloud video datacontained in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the geometry bitstream to reconstruct thepositions (geometry) of points. The point cloud content providing systemmay reconstruct the attributes of the points by decoding the attributebitstream based on the reconstructed geometry. The point cloud contentproviding system (for example, the reception device 10004 or the pointcloud video decoder 10005) may reconstruct the point cloud video basedon the positions according to the reconstructed geometry and the decodedattributes.

The point cloud content providing system according to the embodiments(for example, the reception device 10004 or the renderer 10007) mayrender the decoded point cloud data (20004). The point cloud contentproviding system (for example, the reception device 10004 or therenderer 10007) may render the geometry and attributes decoded throughthe decoding process, using various rendering methods. Points in thepoint cloud content may be rendered to a vertex having a certainthickness, a cube having a specific minimum size centered on thecorresponding vertex position, or a circle centered on the correspondingvertex position. All or part of the rendered point cloud content isprovided to the user through a display (e.g., a VR/AR display, a generaldisplay, etc.).

The point cloud content providing system (for example, the receptiondevice 10004) according to the embodiments may secure feedbackinformation (20005). The point cloud content providing system may encodeand/or decode point cloud data based on the feedback information. Thefeedback information and the operation of the point cloud contentproviding system according to the embodiments are the same as thefeedback information and the operation described with reference to FIG.1 , and thus detailed description thereof is omitted.

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments.

FIG. 3 illustrates an exemplary point cloud video capture process of thepoint cloud content providing system described with reference to FIGS. 1to 2 .

Point cloud content includes a point cloud video (images and/or videos)representing an object and/or environment located in various 3D spaces(e.g., a 3D space representing a real environment, a 3D spacerepresenting a virtual environment, etc.). Accordingly, the point cloudcontent providing system according to the embodiments may capture apoint cloud video using one or more cameras (e.g., an infrared cameracapable of securing depth information, an RGB camera capable ofextracting color information corresponding to the depth information,etc.), a projector (e.g., an infrared pattern projector to secure depthinformation), a LiDAR, or the like. The point cloud content providingsystem according to the embodiments may extract the shape of geometrycomposed of points in a 3D space from the depth information and extractthe attributes of each point from the color information to secure pointcloud data. An image and/or video according to the embodiments may becaptured based on at least one of the inward-facing technique and theoutward-facing technique.

The left part of FIG. 3 illustrates the inward-facing technique. Theinward-facing technique refers to a technique of capturing images acentral object with one or more cameras (or camera sensors) positionedaround the central object. The inward-facing technique may be used togenerate point cloud content providing a 360-degree image of a keyobject to the user (e.g., VR/AR content providing a 360-degree image ofan object (e.g., a key object such as a character, player, object, oractor) to the user).

The right part of FIG. 3 illustrates the outward-facing technique. Theoutward-facing technique refers to a technique of capturing images anenvironment of a central object rather than the central object with oneor more cameras (or camera sensors) positioned around the centralobject. The outward-facing technique may be used to generate point cloudcontent for providing a surrounding environment that appears from theuser's point of view (e.g., content representing an external environmentthat may be provided to a user of a self-driving vehicle).

As shown in FIG. 3 , the point cloud content may be generated based onthe capturing operation of one or more cameras. In this case, thecoordinate system may differ among the cameras, and accordingly thepoint cloud content providing system may calibrate one or more camerasto set a global coordinate system before the capturing operation. Inaddition, the point cloud content providing system may generate pointcloud content by synthesizing an arbitrary image and/or video with animage and/or video captured by the above-described capture technique.The point cloud content providing system may not perform the capturingoperation described in FIG. 3 when it generates point cloud contentrepresenting a virtual space. The point cloud content providing systemaccording to the embodiments may perform post-processing on the capturedimage and/or video. In other words, the point cloud content providingsystem may remove an unwanted area (for example, a background),recognize a space to which the captured images and/or videos areconnected, and, when there is a spatial hole, perform an operation offilling the spatial hole.

The point cloud content providing system may generate one piece of pointcloud content by performing coordinate transformation on points of thepoint cloud video secured from each camera. The point cloud contentproviding system may perform coordinate transformation on the pointsbased on the coordinates of the position of each camera. Accordingly,the point cloud content providing system may generate contentrepresenting one wide range, or may generate point cloud content havinga high density of points.

FIG. 4 illustrates an exemplary point cloud video encoder according toembodiments.

FIG. 4 shows an example of the point cloud video encoder 10002 of FIG. 1. The point cloud video encoder reconstructs and encodes point clouddata (e.g., positions and/or attributes of the points) to adjust thequality of the point cloud content (to, for example, lossless, lossy, ornear-lossless) according to the network condition or applications. Whenthe overall size of the point cloud content is large (e.g., point cloudcontent of 60 Gbps is given for 30 fps), the point cloud contentproviding system may fail to stream the content in real time.Accordingly, the point cloud content providing system may reconstructthe point cloud content based on the maximum target bitrate to providethe same in accordance with the network environment or the like.

As described with reference to FIGS. 1 to 2 , the point cloud videoencoder may perform geometry encoding and attribute encoding. Thegeometry encoding is performed before the attribute encoding.

The point cloud video encoder according to the embodiments includes acoordinate transformer (Transform coordinates) 40000, a quantizer(Quantize and remove points (voxelize)) 40001, an octree analyzer(Analyze octree) 40002, and a surface approximation analyzer (Analyzesurface approximation) 40003, an arithmetic encoder (Arithmetic encode)40004, a geometric reconstructor (Reconstruct geometry) 40005, a colortransformer (Transform colors) 40006, an attribute transformer(Transform attributes) 40007, a RAHT transformer (RAHT) 40008, an LODgenerator (Generate LOD) 40009, a lifting transformer (Lifting) 40010, acoefficient quantizer (Quantize coefficients) 40011, and/or anarithmetic encoder (Arithmetic encode) 40012.

The coordinate transformer 40000, the quantizer 40001, the octreeanalyzer 40002, the surface approximation analyzer 40003, the arithmeticencoder 40004, and the geometry reconstructor 40005 may perform geometryencoding. The geometry encoding according to the embodiments may includeoctree geometry coding, direct coding, trisoup geometry encoding, andentropy encoding. The direct coding and trisoup geometry encoding areapplied selectively or in combination. The geometry encoding is notlimited to the above-described example.

As shown in the figure, the coordinate transformer 40000 according tothe embodiments receives positions and transforms the same intocoordinates. For example, the positions may be transformed into positioninformation in a three-dimensional space (for example, athree-dimensional space represented by an XYZ coordinate system). Theposition information in the three-dimensional space according to theembodiments may be referred to as geometry information.

The quantizer 40001 according to the embodiments quantizes the geometryinformation. For example, the quantizer 40001 may quantize the pointsbased on a minimum position value of all points (for example, a minimumvalue on each of the X, Y, and Z axes). The quantizer 40001 performs aquantization operation of multiplying the difference between the minimumposition value and the position value of each point by a presetquantization scale value and then finding the nearest integer value byrounding the value obtained through the multiplication. Thus, one ormore points may have the same quantized position (or position value).The quantizer 40001 according to the embodiments performs voxelizationbased on the quantized positions to reconstruct quantized points. Thevoxelization means a minimum unit representing position information in3D spacePoints of point cloud content (or 3D point cloud video)according to the embodiments may be included in one or more voxels. Theterm voxel, which is a compound of volume and pixel, refers to a 3Dcubic space generated when a 3D space is divided into units (unit=1.0)based on the axes representing the 3D space (e.g., X-axis, Y-axis, andZ-axis). The quantizer 40001 may match groups of points in the 3D spacewith voxels. According to embodiments, one voxel may include only onepoint. According to embodiments, one voxel may include one or morepoints. In order to express one voxel as one point, the position of thecenter point of a voxel may be set based on the positions of one or morepoints included in the voxel. In this case, attributes of all positionsincluded in one voxel may be combined and assigned to the voxel.

The octree analyzer 40002 according to the embodiments performs octreegeometry coding (or octree coding) to present voxels in an octreestructure. The octree structure represents points matched with voxels,based on the octal tree structure.

The surface approximation analyzer 40003 according to the embodimentsmay analyze and approximate the octree. The octree analysis andapproximation according to the embodiments is a process of analyzing aregion containing a plurality of points to efficiently provide octreeand voxelization.

The arithmetic encoder 40004 according to the embodiments performsentropy encoding on the octree and/or the approximated octree. Forexample, the encoding scheme includes arithmetic encoding. As a resultof the encoding, a geometry bitstream is generated.

The color transformer 40006, the attribute transformer 40007, the RAHTtransformer 40008, the LOD generator 40009, the lifting transformer40010, the coefficient quantizer 40011, and/or the arithmetic encoder40012 perform attribute encoding. As described above, one point may haveone or more attributes. The attribute encoding according to theembodiments is equally applied to the attributes that one point has.However, when an attribute (e.g., color) includes one or more elements,attribute encoding is independently applied to each element. Theattribute encoding according to the embodiments includes color transformcoding, attribute transform coding, region adaptive hierarchicaltransform (RAHT) coding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) coding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) coding. Depending on the pointcloud content, the RAHT coding, the prediction transform coding and thelifting transform coding described above may be selectively used, or acombination of one or more of the coding schemes may be used. Theattribute encoding according to the embodiments is not limited to theabove-described example.

The color transformer 40006 according to the embodiments performs colortransform coding of transforming color values (or textures) included inthe attributes. For example, the color transformer 40006 may transformthe format of color information (for example, from RGB to YCbCr). Theoperation of the color transformer 40006 according to embodiments may beoptionally applied according to the color values included in theattributes.

The geometry reconstructor 40005 according to the embodimentsreconstructs (decompresses) the octree and/or the approximated octree.The geometry reconstructor 40005 reconstructs the octree/voxels based onthe result of analyzing the distribution of points. The reconstructedoctree/voxels may be referred to as reconstructed geometry (restoredgeometry).

The attribute transformer 40007 according to the embodiments performsattribute transformation to transform the attributes based on thereconstructed geometry and/or the positions on which geometry encodingis not performed. As described above, since the attributes are dependenton the geometry, the attribute transformer 40007 may transform theattributes based on the reconstructed geometry information. For example,based on the position value of a point included in a voxel, theattribute transformer 40007 may transform the attribute of the point atthe position. As described above, when the position of the center of avoxel is set based on the positions of one or more points included inthe voxel, the attribute transformer 40007 transforms the attributes ofthe one or more points. When the trisoup geometry encoding is performed,the attribute transformer 40007 may transform the attributes based onthe trisoup geometry encoding.

The attribute transformer 40007 may perform the attribute transformationby calculating the average of attributes or attribute values ofneighboring points (e.g., color or reflectance of each point) within aspecific position/radius from the position (or position value) of thecenter of each voxel. The attribute transformer 40007 may apply a weightaccording to the distance from the center to each point in calculatingthe average. Accordingly, each voxel has a position and a calculatedattribute (or attribute value).

The attribute transformer 40007 may search for neighboring pointsexisting within a specific position/radius from the position of thecenter of each voxel based on the K-D tree or the Morton code. The K-Dtree is a binary search tree and supports a data structure capable ofmanaging points based on the positions such that nearest neighbor search(NNS) can be performed quickly. The Morton code is generated bypresenting coordinates (e.g., (x, y, z)) representing 3D positions ofall points as bit values and mixing the bits. For example, when thecoordinates representing the position of a point are (5, 9, 1), the bitvalues for the coordinates are (0101, 1001, 0001). Mixing the bit valuesaccording to the bit index in order of z, y, and x yields 010001000111.This value is expressed as a decimal number of 1095. That is, the Mortoncode value of the point having coordinates (5, 9, 1) is 1095. Theattribute transformer 40007 may order the points based on the Mortoncode values and perform NNS through a depth-first traversal process.After the attribute transformation operation, the K-D tree or the Mortoncode is used when the NNS is needed in another transformation processfor attribute coding.

As shown in the figure, the transformed attributes are input to the RAHTtransformer 40008 and/or the LOD generator 40009.

The RAHT transformer 40008 according to the embodiments performs RAHTcoding for predicting attribute information based on the reconstructedgeometry information. For example, the RAHT transformer 40008 maypredict attribute information of a node at a higher level in the octreebased on the attribute information associated with a node at a lowerlevel in the octree.

The LOD generator 40009 according to the embodiments generates a levelof detail (LOD). The LOD according to the embodiments is a degree ofdetail of point cloud content. As the LOD value decrease, it indicatesthat the detail of the point cloud content is degraded. As the LOD valueincreases, it indicates that the detail of the point cloud content isenhanced. Points may be classified by the LOD.

The lifting transformer 40010 according to the embodiments performslifting transform coding of transforming the attributes a point cloudbased on weights. As described above, lifting transform coding may beoptionally applied.

The coefficient quantizer 40011 according to the embodiments quantizesthe attribute-coded attributes based on coefficients.

The arithmetic encoder 40012 according to the embodiments encodes thequantized attributes based on arithmetic coding.

Although not shown in the figure, the elements of the point cloud videoencoder of FIG. 4 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud content providing apparatus,software, firmware, or a combination thereof. The one or more processorsmay perform at least one of the operations and/or functions of theelements of the point cloud video encoder of FIG. 4 described above.Additionally, the one or more processors may operate or execute a set ofsoftware programs and/or instructions for performing the operationsand/or functions of the elements of the point cloud video encoder ofFIG. 4 . The one or more memories according to the embodiments mayinclude a high speed random access memory, or include a non-volatilememory (e.g., one or more magnetic disk storage devices, flash memorydevices, or other non-volatile solid-state memory devices).

FIG. 5 shows an example of voxels according to embodiments.

FIG. 5 shows voxels positioned in a 3D space represented by a coordinatesystem composed of three axes, which are the X-axis, the Y-axis, and theZ-axis. As described with reference to FIG. 4 , the point cloud videoencoder (e.g., the quantizer 40001) may perform voxelization. Voxelrefers to a 3D cubic space generated when a 3D space is divided intounits (unit=1.0) based on the axes representing the 3D space (e.g.,X-axis, Y-axis, and Z-axis). FIG. 5 shows an example of voxels generatedthrough an octree structure in which a cubical axis-aligned bounding boxdefined by two poles (0, 0, 0) and (2^(d), 2^(d), 2^(d)) is recursivelysubdivided. One voxel includes at least one point. The spatialcoordinates of a voxel may be estimated from the positional relationshipwith a voxel group. As described above, a voxel has an attribute (suchas color or reflectance) like pixels of a 2D image/video. The details ofthe voxel are the same as those described with reference to FIG. 4 , andtherefore a description thereof is omitted.

FIG. 6 shows an example of an octree and occupancy code according toembodiments.

As described with reference to FIGS. 1 to 4 , the point cloud contentproviding system (point cloud video encoder 10002) or the octreeanalyzer 40002 of the point cloud video encoder performs octree geometrycoding (or octree coding) based on an octree structure to efficientlymanage the region and/or position of the voxel.

The upper part of FIG. 6 shows an octree structure. The 3D space of thepoint cloud content according to the embodiments is represented by axes(e.g., X-axis, Y-axis, and Z-axis) of the coordinate system. The octreestructure is created by recursive subdividing of a cubical axis-alignedbounding box defined by two poles (0, 0, 0) and (2^(d), 2^(d), 2^(d)).Here, 2^(d) may be set to a value constituting the smallest bounding boxsurrounding all points of the point cloud content (or point cloudvideo). Here, d denotes the depth of the octree. The value of d isdetermined in Equation 1. In Equation 1, (x^(int) _(n), y^(int) _(n),z^(int) _(n)) denotes the positions (or position values) of quantizedpoints.d=Ceil(Log 2(Max(x _(n) ^(int) ,y _(n) ^(int) ,z _(n) ^(int) ,n=1, . . .,N)+1))  Equation 1

As shown in the middle of the upper part of FIG. 6 , the entire 3D spacemay be divided into eight spaces according to partition. Each dividedspace is represented by a cube with six faces. As shown in the upperright of FIG. 6 , each of the eight spaces is divided again based on theaxes of the coordinate system (e.g., X-axis, Y-axis, and Z-axis).Accordingly, each space is divided into eight smaller spaces. Thedivided smaller space is also represented by a cube with six faces. Thispartitioning scheme is applied until the leaf node of the octree becomesa voxel.

The lower part of FIG. 6 shows an octree occupancy code. The occupancycode of the octree is generated to indicate whether each of the eightdivided spaces generated by dividing one space contains at least onepoint. Accordingly, a single occupancy code is represented by eightchild nodes. Each child node represents the occupancy of a dividedspace, and the child node has a value in 1 bit. Accordingly, theoccupancy code is represented as an 8-bit code. That is, when at leastone point is contained in the space corresponding to a child node, thenode is assigned a value of 1. When no point is contained in the spacecorresponding to the child node (the space is empty), the node isassigned a value of 0. Since the occupancy code shown in FIG. 6 is00100001, it indicates that the spaces corresponding to the third childnode and the eighth child node among the eight child nodes each containat least one point. As shown in the figure, each of the third child nodeand the eighth child node has eight child nodes, and the child nodes arerepresented by an 8-bit occupancy code. The figure shows that theoccupancy code of the third child node is 10000111, and the occupancycode of the eighth child node is 01001111. The point cloud video encoder(for example, the arithmetic encoder 40004) according to the embodimentsmay perform entropy encoding on the occupancy codes. In order toincrease the compression efficiency, the point cloud video encoder mayperform intra/inter-coding on the occupancy codes. The reception device(for example, the reception device 10004 or the point cloud videodecoder 10006) according to the embodiments reconstructs the octreebased on the occupancy codes.

The point cloud video encoder (for example, the octree analyzer 40002)according to the embodiments may perform voxelization and octree codingto store the positions of points. However, points are not always evenlydistributed in the 3D space, and accordingly there may be a specificregion in which fewer points are present. Accordingly, it is inefficientto perform voxelization for the entire 3D space. For example, when aspecific region contains few points, voxelization does not need to beperformed in the specific region.

Accordingly, for the above-described specific region (or a node otherthan the leaf node of the octree), the point cloud video encoderaccording to the embodiments may skip voxelization and perform directcoding to directly code the positions of points included in the specificregion. The coordinates of a direct coding point according to theembodiments are referred to as direct coding mode (DCM). The point cloudvideo encoder according to the embodiments may also perform trisoupgeometry encoding, which is to reconstruct the positions of the pointsin the specific region (or node) based on voxels, based on a surfacemodel. The trisoup geometry encoding is geometry encoding thatrepresents an object as a series of triangular meshes. Accordingly, thepoint cloud video decoder may generate a point cloud from the meshsurface. The direct coding and trisoup geometry encoding according tothe embodiments may be selectively performed. In addition, the directcoding and trisoup geometry encoding according to the embodiments may beperformed in combination with octree geometry coding (or octree coding).

To perform direct coding, the option to use the direct mode for applyingdirect coding should be activated. A node to which direct coding is tobe applied is not a leaf node, and points less than a threshold shouldbe present within a specific node. In addition, the total number ofpoints to which direct coding is to be applied should not exceed apreset threshold. When the conditions above are satisfied, the pointcloud video encoder (or the arithmetic encoder 40004) according to theembodiments may perform entropy coding on the positions (or positionvalues) of the points.

The point cloud video encoder (for example, the surface approximationanalyzer 40003) according to the embodiments may determine a specificlevel of the octree (a level less than the depth d of the octree), andthe surface model may be used staring with that level to perform trisoupgeometry encoding to reconstruct the positions of points in the regionof the node based on voxels (Trisoup mode). The point cloud videoencoder according to the embodiments may specify a level at whichtrisoup geometry encoding is to be applied. For example, when thespecific level is equal to the depth of the octree, the point cloudvideo encoder does not operate in the trisoup mode. In other words, thepoint cloud video encoder according to the embodiments may operate inthe trisoup mode only when the specified level is less than the value ofdepth of the octree. The 3D cube region of the nodes at the specifiedlevel according to the embodiments is called a block. One block mayinclude one or more voxels. The block or voxel may correspond to abrick. Geometry is represented as a surface within each block. Thesurface according to embodiments may intersect with each edge of a blockat most once.

One block has 12 edges, and accordingly there are at least 12intersections in one block. Each intersection is called a vertex (orapex). A vertex present along an edge is detected when there is at leastone occupied voxel adjacent to the edge among all blocks sharing theedge. The occupied voxel according to the embodiments refers to a voxelcontaining a point. The position of the vertex detected along the edgeis the average position along the edge of all voxels adjacent to theedge among all blocks sharing the edge.

Once the vertex is detected, the point cloud video encoder according tothe embodiments may perform entropy encoding on the starting point (x,y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, andthe vertex position value (relative position value within the edge).When the trisoup geometry encoding is applied, the point cloud videoencoder according to the embodiments (for example, the geometryreconstructor 40005) may generate restored geometry (reconstructedgeometry) by performing the triangle reconstruction, up-sampling, andvoxelization processes.

The vertices positioned at the edge of the block determine a surfacethat passes through the block. The surface according to the embodimentsis a non-planar polygon. In the triangle reconstruction process, asurface represented by a triangle is reconstructed based on the startingpoint of the edge, the direction vector of the edge, and the positionvalues of the vertices. The triangle reconstruction process is performedaccording to Equation 2 by: i) calculating the centroid value of eachvertex, ii) subtracting the center value from each vertex value, andiii) estimating the sum of the squares of the values obtained by thesubtraction.

$\begin{matrix}{{Equation}\mspace{14mu} 2} & \; \\{\begin{bmatrix}\mu_{x} \\\mu_{y} \\\mu_{z}\end{bmatrix} = {\frac{1}{n}{\sum_{i = 1}^{n}\begin{bmatrix}x_{i} \\y_{i} \\z_{i}\end{bmatrix}}}} & 1 \\{\begin{bmatrix}{\overset{\_}{x}}_{i} \\{\overset{\_}{y}}_{i} \\{\overset{\_}{z}}_{i}\end{bmatrix} = {\begin{bmatrix}x_{i} \\y_{i} \\z_{i}\end{bmatrix} - \begin{bmatrix}\mu_{x} \\\mu_{y} \\\mu_{z}\end{bmatrix}}} & 2 \\{\begin{bmatrix}\sigma_{x}^{2} \\\sigma_{y}^{2} \\\sigma_{z}^{2}\end{bmatrix} = {\sum_{i = 1}^{n}\begin{bmatrix}{\overset{\_}{x}}_{i}^{2} \\{\overset{\_}{y}}_{i}^{2} \\{\overset{\_}{z}}_{i}^{2}\end{bmatrix}}} & 3\end{matrix}$

Then, the minimum value of the sum is estimated, and the projectionprocess is performed according to the axis with the minimum value. Forexample, when the element x is the minimum, each vertex is projected onthe x-axis with respect to the center of the block, and projected on the(y, z) plane. When the values obtained through projection on the (y, z)plane are (ai, bi), the value of 0 is estimated through a tan 2(bi, ai),and the vertices are ordered based on the value of 0. The table 1 belowshows a combination of vertices for creating a triangle according to thenumber of the vertices. The vertices are ordered from 1 to n. The table1 below shows that for four vertices, two triangles may be constructedaccording to combinations of vertices. The first triangle may consist ofvertices 1, 2, and 3 among the ordered vertices, and the second trianglemay consist of vertices 3, 4, and 1 among the ordered vertices.

TABLE 1 Triangles formed from vertices ordered 1, . . . ,n n Triangles 3(1,2,3) 4 (1,2,3), (3,4,1) 5 (1,2,3), (3,4,5), (5,1,3) 6 (1,2,3),(3,4,5), (5,6,1), (1,3,5) 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1) 9 (1,2,3),(3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3) 10 (1,2,3),(3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5) 11(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7),(7,9,11), (11,3,7) 12 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11),(11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

The upsampling process is performed to add points in the middle alongthe edge of the triangle and perform voxelization. The added points aregenerated based on the upsampling factor and the width of the block. Theadded points are called refined vertices. The point cloud video encoderaccording to the embodiments may voxelize the refined vertices. Inaddition, the point cloud video encoder may perform attribute encodingbased on the voxelized positions (or position values).

FIG. 7 shows an example of a neighbor node pattern according toembodiments.

In order to increase the compression efficiency of the point cloudvideo, the point cloud video encoder according to the embodiments mayperform entropy coding based on context adaptive arithmetic coding.

As described with reference to FIGS. 1 to 6 , the point cloud contentproviding system or the point cloud video encoder 10002 of FIG. 1 , orthe point cloud video encoder or arithmetic encoder 40004 of FIG. 4 mayperform entropy coding on the occupancy code immediately. In addition,the point cloud content providing system or the point cloud videoencoder may perform entropy encoding (intra encoding) based on theoccupancy code of the current node and the occupancy of neighboringnodes, or perform entropy encoding (inter encoding) based on theoccupancy code of the previous frame. A frame according to embodimentsrepresents a set of point cloud videos generated at the same time. Thecompression efficiency of intra encoding/inter encoding according to theembodiments may depend on the number of neighboring nodes that arereferenced. When the bits increase, the operation becomes complicated,but the encoding may be biased to one side, which may increase thecompression efficiency. For example, when a 3-bit context is given,coding needs to be performed using 2³=8 methods. The part divided forcoding affects the complexity of implementation. Accordingly, it isnecessary to meet an appropriate level of compression efficiency andcomplexity.

FIG. 7 illustrates a process of obtaining an occupancy pattern based onthe occupancy of neighbor nodes. The point cloud video encoder accordingto the embodiments determines occupancy of neighbor nodes of each nodeof the octree and obtains a value of a neighbor pattern. The neighbornode pattern is used to infer the occupancy pattern of the node. The uppart of FIG. 7 shows a cube corresponding to a node (a cube positionedin the middle) and six cubes (neighbor nodes) sharing at least one facewith the cube. The nodes shown in the figure are nodes of the samedepth. The numbers shown in the figure represent weights (1, 2, 4, 8,16, and 32) associated with the six nodes, respectively. The weights areassigned sequentially according to the positions of neighboring nodes.

The down part of FIG. 7 shows neighbor node pattern values. A neighbornode pattern value is the sum of values multiplied by the weight of anoccupied neighbor node (a neighbor node having a point). Accordingly,the neighbor node pattern values are 0 to 63. When the neighbor nodepattern value is 0, it indicates that there is no node having a point(no occupied node) among the neighbor nodes of the node. When theneighbor node pattern value is 63, it indicates that all neighbor nodesare occupied nodes. As shown in the figure, since neighbor nodes towhich weights 1, 2, 4, and 8 are assigned are occupied nodes, theneighbor node pattern value is 15, the sum of 1, 2, 4, and 8. The pointcloud video encoder may perform coding according to the neighbor nodepattern value (for example, when the neighbor node pattern value is 63,64 kinds of coding may be performed). According to embodiments, thepoint cloud video encoder may reduce coding complexity by changing aneighbor node pattern value (for example, based on a table by which 64is changed to 10 or 6).

FIG. 8 illustrates an example of point configuration in each LODaccording to embodiments.

As described with reference to FIGS. 1 to 7 , encoded geometry isreconstructed (decompressed) before attribute encoding is performed.When direct coding is applied, the geometry reconstruction operation mayinclude changing the placement of direct coded points (e.g., placing thedirect coded points in front of the point cloud data). When trisoupgeometry encoding is applied, the geometry reconstruction process isperformed through triangle reconstruction, up-sampling, andvoxelization. Since the attribute depends on the geometry, attributeencoding is performed based on the reconstructed geometry.

The point cloud video encoder (for example, the LOD generator 40009) mayclassify (reorganize or group) points by LOD. FIG. 8 shows the pointcloud content corresponding to LODs. The leftmost picture in FIG. 8represents original point cloud content. The second picture from theleft of FIG. 8 represents distribution of the points in the lowest LOD,and the rightmost picture in FIG. 8 represents distribution of thepoints in the highest LOD. That is, the points in the lowest LOD aresparsely distributed, and the points in the highest LOD are denselydistributed. That is, as the LOD rises in the direction pointed by thearrow indicated at the bottom of FIG. 8 , the space (or distance)between points is narrowed.

FIG. 9 illustrates an example of point configuration for each LODaccording to embodiments.

As described with reference to FIGS. 1 to 8 , the point cloud contentproviding system, or the point cloud video encoder (for example, thepoint cloud video encoder 10002 of FIG. 1 , the point cloud videoencoder of FIG. 4 , or the LOD generator 40009) may generates an LOD.The LOD is generated by reorganizing the points into a set of refinementlevels according to a set LOD distance value (or a set of Euclideandistances). The LOD generation process is performed not only by thepoint cloud video encoder, but also by the point cloud video decoder.

The upper part of FIG. 9 shows examples (P0 to P9) of points of thepoint cloud content distributed in a 3D space. In FIG. 9 , the originalorder represents the order of points P0 to P9 before LOD generation. InFIG. 9 , the LOD based order represents the order of points according tothe LOD generation. Points are reorganized by LOD. Also, a high LODcontains the points belonging to lower LODs. As shown in FIG. 9 , LOD0contains P0, P5, P4 and P2. LOD1 contains the points of LOD0, P1, P6 andP3. LOD2 contains the points of LOD0, the points of LOD1, P9, P8 and P7.

As described with reference to FIG. 4 , the point cloud video encoderaccording to the embodiments may perform prediction transform codingbased on LOD, lifting transform coding based on LOD, and RAHT transformcoding selectively or in combination.

The point cloud video encoder according to the embodiments may generatea predictor for points to perform prediction transform coding based onLOD for setting a predicted attribute (or predicted attribute value) ofeach point. That is, N predictors may be generated for N points. Thepredictor according to the embodiments may calculate a weight(=1/distance) based on the LOD value of each point, indexing informationabout neighboring points present within a set distance for each LOD, anda distance to the neighboring points.

The predicted attribute (or attribute value) according to theembodiments is set to the average of values obtained by multiplying theattributes (or attribute values) (e.g., color, reflectance, etc.) ofneighbor points set in the predictor of each point by a weight (orweight value) calculated based on the distance to each neighbor point.The point cloud video encoder according to the embodiments (for example,the coefficient quantizer 40011) may quantize and inversely quantize theresidual of each point (which may be called residual attribute, residualattribute value, attribute prediction residual value or prediction errorattribute value and so on) obtained by subtracting a predicted attribute(or attribute value) each point from the attribute (i.e., originalattribute value) of each point. The quantization process performed for aresidual attribute value in a transmission device is configured as shownin table 2. The inverse quantization process performed for a residualattribute value in a reception device is configured as shown in table 3.

TABLE 2 int PCCQuantization(int value, int quantStep) { if( value >=0) {return floor(value / quantStep + 1.0 / 3.0); } else { return-floor(-value / quantStep + 1.0 / 3.0); } }

TABLE 3 int PCCInverseQuantization(int value, int quantStep) { if(quantStep==0) { return value; } else { return value * quantStep; } }

When the predictor of each point has neighbor points, the point cloudvideo encoder (e.g., the arithmetic encoder 40012) according to theembodiments may perform entropy coding on the quantized and inverselyquantized residual attribute values as described above. When thepredictor of each point has no neighbor point, the point cloud videoencoder according to the embodiments (for example, the arithmeticencoder 40012) may perform entropy coding on the attributes of thecorresponding point without performing the above-described operation.

The point cloud video encoder according to the embodiments (for example,the lifting transformer 40010) may generate a predictor of each point,set the calculated LOD and register neighbor points in the predictor,and set weights according to the distances to neighbor points to performlifting transform coding. The lifting transform coding according to theembodiments is similar to the above-described prediction transformcoding, but differs therefrom in that weights are cumulatively appliedto attribute values. The process of cumulatively applying weights to theattribute values according to embodiments is configured as follows.

1) Create an array Quantization Weight (QW) for storing the weight valueof each point. The initial value of all elements of QW is 1.0. Multiplythe QW values of the predictor indexes of the neighbor nodes registeredin the predictor by the weight of the predictor of the current point,and add the values obtained by the multiplication.

2) Lift prediction process: Subtract the value obtained by multiplyingthe attribute value of the point by the weight from the existingattribute value to calculate a predicted attribute value.

3) Create temporary arrays called updateweight and update and initializethe temporary arrays to zero.

4) Cumulatively add the weights calculated by multiplying the weightscalculated for all predictors by a weight stored in the QW correspondingto a predictor index to the updateweight array as indexes of neighbornodes. Cumulatively add, to the update array, a value obtained bymultiplying the attribute value of the index of a neighbor node by thecalculated weight.

5) Lift update process: Divide the attribute values of the update arrayfor all predictors by the weight value of the updateweight array of thepredictor index, and add the existing attribute value to the valuesobtained by the division.

6) Calculate predicted attributes by multiplying the attribute valuesupdated through the lift update process by the weight updated throughthe lift prediction process (stored in the QW) for all predictors. Thepoint cloud video encoder (e.g., coefficient quantizer 40011) accordingto the embodiments quantizes the predicted attribute values. Inaddition, the point cloud video encoder (e.g., the arithmetic encoder40012) performs entropy coding on the quantized attribute values.

The point cloud video encoder (for example, the RAHT transformer 40008)according to the embodiments may perform RAHT transform coding in whichattributes of nodes of a higher level are predicted using the attributesassociated with nodes of a lower level in the octree. RAHT transformcoding is an example of attribute intra coding through an octreebackward scan. The point cloud video encoder according to theembodiments scans the entire region from the voxel and repeats themerging process of merging the voxels into a larger block at each stepuntil the root node is reached. The merging process according to theembodiments is performed only on the occupied nodes. The merging processis not performed on the empty node. The merging process is performed onan upper node immediately above the empty node.

Equation 3 below represents a RAHT transformation matrix. In Equation 3,g_(l) _(x,y,z) denotes the average attribute value of voxels at level l.g_(l) _(x,y,z) may be calculated based on g_(l+1) _(2x,y,z) and g_(l+1)_(2x+1,y,z) . The weights for g_(l) _(2x,y,z) and g_(l) _(2x+1,y,z) arew1=w_(l) _(2x,y,z) and w2=w_(l) _(2x+1,y,z) .

$\begin{matrix}{\mspace{79mu}{{Equation}\mspace{14mu} 3}} & \; \\{\left\lceil \begin{matrix}g_{l - 1_{x,y,z}} \\h_{l - 1_{x,y,z}}\end{matrix} \right\rceil = {{T_{w1w2}\left\lceil \begin{matrix}g_{l_{{2x},y,z}} \\g_{l_{{{2x} + 1},y,z}}\end{matrix} \right\rceil\mspace{31mu} T_{w1w2}} = {\frac{1}{\sqrt{{w1} + {w2}}}\begin{bmatrix}\sqrt{w1} & \sqrt{w2} \\{- \sqrt{w2}} & \sqrt{w1}\end{bmatrix}}}} & \;\end{matrix}$

Here, g_(l−1) _(x,y,z) is a low-pass value and is used in the mergingprocess at the next higher level. h_(l−1) _(x,y,z) denotes high-passcoefficients. The high-pass coefficients at each step are quantized andsubjected to entropy coding (for example, encoding by the arithmeticencoder 40012). The weights are calculated as w_(l) _(−1 x,y,z) =w_(l)_(2x,y,z) +w_(l) _(2x+1,y,z) . The root node is created through the g₁_(0,0,0) and g₁ _(0,0,1) as Equation 4.

$\begin{matrix}{{Equation}\mspace{14mu} 4} & \; \\{\left\lceil \begin{matrix}{gDC} \\h_{0_{0,0,0}}\end{matrix} \right\rceil = {T_{w\; 1000\mspace{14mu} w\; 1001}\left\lceil \begin{matrix}g_{1_{0,{00Z}}} \\g_{1_{0,0,1}}\end{matrix} \right\rceil}} & \;\end{matrix}$

The value of gDC is also quantized and subjected to entropy coding likethe high-pass coefficients.

FIG. 10 illustrates a point cloud video decoder according toembodiments.

The point cloud video decoder illustrated in FIG. 10 is an example ofthe point cloud video decoder 10006 described in FIG. 1 , and mayperform the same or similar operations as the operations of the pointcloud video decoder 10006 illustrated in FIG. 1 . As shown in thefigure, the point cloud video decoder may receive a geometry bitstreamand an attribute bitstream contained in one or more bitstreams. Thepoint cloud video decoder includes a geometry decoder and an attributedecoder. The geometry decoder performs geometry decoding on the geometrybitstream and outputs decoded geometry. The attribute decoder performsattribute decoding on the attribute bitstream based on the decodedgeometry, and outputs decoded attributes. The decoded geometry anddecoded attributes are used to reconstruct point cloud content (adecoded point cloud).

FIG. 11 illustrates a point cloud video decoder according toembodiments.

The point cloud video decoder illustrated in FIG. 11 is an example ofthe point cloud video decoder illustrated in FIG. 10 , and may perform adecoding operation, which is an inverse process of the encodingoperation of the point cloud video encoder illustrated in FIGS. 1 to 9 .

As described with reference to FIGS. 1 and 10 , the point cloud videodecoder may perform geometry decoding and attribute decoding. Thegeometry decoding is performed before the attribute decoding.

The point cloud video decoder according to the embodiments includes anarithmetic decoder (Arithmetic decode) 11000, an octree synthesizer(Synthesize octree) 11001, a surface approximation synthesizer(Synthesize surface approximation) 11002, and a geometry reconstructor(Reconstruct geometry) 11003, a coordinate inverse transformer (Inversetransform coordinates) 11004, an arithmetic decoder (Arithmetic decode)11005, an inverse quantizer (Inverse quantize) 11006, a RAHT transformer11007, an LOD generator (Generate LOD) 11008, an inverse lifter (inverselifting) 11009, and/or a color inverse transformer (Inverse transformcolors) 11010.

The arithmetic decoder 11000, the octree synthesizer 11001, the surfaceapproximation synthesizer 11002, and the geometry reconstructor 11003,and the coordinate inverse transformer 11004 may perform geometrydecoding. The geometry decoding according to the embodiments may includedirect decoding and trisoup geometry decoding. The direct decoding andtrisoup geometry decoding are selectively applied. The geometry decodingis not limited to the above-described example, and is performed as aninverse process of the geometry encoding described with reference toFIGS. 1 to 9 .

The arithmetic decoder 11000 according to the embodiments decodes thereceived geometry bitstream based on the arithmetic coding. Theoperation of the arithmetic decoder 11000 corresponds to the inverseprocess of the arithmetic encoder 40004.

The octree synthesizer 11001 according to the embodiments may generatean octree by acquiring an occupancy code from the decoded geometrybitstream (or information on the geometry secured as a result ofdecoding). The occupancy code is configured as described in detail withreference to FIGS. 1 to 9 .

When the trisoup geometry encoding is applied, the surface approximationsynthesizer 11002 according to the embodiments may synthesize a surfacebased on the decoded geometry and/or the generated octree.

The geometry reconstructor 11003 according to the embodiments mayregenerate geometry based on the surface and/or the decoded geometry. Asdescribed with reference to FIGS. 1 to 9 , direct coding and trisoupgeometry encoding are selectively applied. Accordingly, the geometryreconstructor 11003 directly imports and adds position information aboutthe points to which direct coding is applied. When the trisoup geometryencoding is applied, the geometry reconstructor 11003 may reconstructthe geometry by performing the reconstruction operations of the geometryreconstructor 40005, for example, triangle reconstruction, up-sampling,and voxelization. Details are the same as those described with referenceto FIG. 6 , and thus description thereof is omitted. The reconstructedgeometry may include a point cloud picture or frame that does notcontain attributes.

The coordinate inverse transformer 11004 according to the embodimentsmay acquire positions of the points by transforming the coordinatesbased on the reconstructed geometry.

The arithmetic decoder 11005, the inverse quantizer 11006, the RAHTtransformer 11007, the LOD generator 11008, the inverse lifter 11009,and/or the color inverse transformer 11010 may perform the attributedecoding described with reference to FIG. 10 . The attribute decodingaccording to the embodiments includes region adaptive hierarchicaltransform (RAHT) decoding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) decoding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) decoding. The three decodingschemes described above may be used selectively, or a combination of oneor more decoding schemes may be used. The attribute decoding accordingto the embodiments is not limited to the above-described example.

The arithmetic decoder 11005 according to the embodiments decodes theattribute bitstream by arithmetic coding.

The inverse quantizer 11006 according to the embodiments inverselyquantizes the information about the decoded attribute bitstream orattributes secured as a result of the decoding, and outputs theinversely quantized attributes (or attribute values). The inversequantization may be selectively applied based on the attribute encodingof the point cloud video encoder.

According to embodiments, the RAHT transformer 11007, the LOD generator11008, and/or the inverse lifter 11009 may process the reconstructedgeometry and the inversely quantized attributes. As described above, theRAHT transformer 11007, the LOD generator 11008, and/or the inverselifter 11009 may selectively perform a decoding operation correspondingto the encoding of the point cloud video encoder.

The color inverse transformer 11010 according to the embodimentsperforms inverse transform coding to inversely transform a color value(or texture) included in the decoded attributes. The operation of thecolor inverse transformer 11010 may be selectively performed based onthe operation of the color transformer 40006 of the point cloud videoencoder.

Although not shown in the figure, the elements of the point cloud videodecoder of FIG. 11 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud content providing apparatus,software, firmware, or a combination thereof. The one or more processorsmay perform at least one or more of the operations and/or functions ofthe elements of the point cloud video decoder of FIG. 11 describedabove. Additionally, the one or more processors may operate or execute aset of software programs and/or instructions for performing theoperations and/or functions of the elements of the point cloud videodecoder of FIG. 11 .

FIG. 12 illustrates a transmission device according to embodiments.

The transmission device shown in FIG. 12 is an example of thetransmission device 10000 of FIG. 1 (or the point cloud video encoder ofFIG. 4 ). The transmission device illustrated in FIG. 12 may perform oneor more of the operations and methods the same as or similar to those ofthe point cloud video encoder described with reference to FIGS. 1 to 9 .The transmission device according to the embodiments may include a datainput unit 12000, a quantization processor 12001, a voxelizationprocessor 12002, an octree occupancy code generator 12003, a surfacemodel processor 12004, an intra/inter-coding processor 12005, anarithmetic coder 12006, a metadata processor 12007, a color transformprocessor 12008, an attribute transform processor 12009, aprediction/lifting/RAHT transform processor 12010, an arithmetic coder12011 and/or a transmission processor 12012.

The data input unit 12000 according to the embodiments receives oracquires point cloud data. The data input unit 12000 may perform anoperation and/or acquisition method the same as or similar to theoperation and/or acquisition method of the point cloud video acquisitionunit 10001 (or the acquisition process 20000 described with reference toFIG. 2 ).

The data input unit 12000, the quantization processor 12001, thevoxelization processor 12002, the octree occupancy code generator 12003,the surface model processor 12004, the intra/inter-coding processor12005, and the arithmetic coder 12006 perform geometry encoding. Thegeometry encoding according to the embodiments is the same as or similarto the geometry encoding described with reference to FIGS. 1 to 9 , andthus a detailed description thereof is omitted.

The quantization processor 12001 according to the embodiments quantizesgeometry (e.g., position values of points). The operation and/orquantization of the quantization processor 12001 is the same as orsimilar to the operation and/or quantization of the quantizer 40001described with reference to FIG. 4 . Details are the same as thosedescribed with reference to FIGS. 1 to 9 .

The voxelization processor 12002 according to the embodiments voxelizesthe quantized position values of the points. The voxelization processor12002 may perform an operation and/or process the same or similar to theoperation and/or the voxelization process of the quantizer 40001described with reference to FIG. 4 . Details are the same as thosedescribed with reference to FIGS. 1 to 9 .

The octree occupancy code generator 12003 according to the embodimentsperforms octree coding on the voxelized positions of the points based onan octree structure. The octree occupancy code generator 12003 maygenerate an occupancy code. The octree occupancy code generator 12003may perform an operation and/or method the same as or similar to theoperation and/or method of the point cloud video encoder (or the octreeanalyzer 40002) described with reference to FIGS. 4 and 6 . Details arethe same as those described with reference to FIGS. 1 to 9 .

The surface model processor 12004 according to the embodiments mayperform trigsoup geometry encoding based on a surface model toreconstruct the positions of points in a specific region (or node) on avoxel basis. The surface model processor 12004 may perform an operationand/or method the same as or similar to the operation and/or method ofthe point cloud video encoder (for example, the surface approximationanalyzer 40003) described with reference to FIG. 4 . Details are thesame as those described with reference to FIGS. 1 to 9 .

The intra/inter-coding processor 12005 according to the embodiments mayperform intra/inter-coding on point cloud data. The intra/inter-codingprocessor 12005 may perform coding the same as or similar to theintra/inter-coding described with reference to FIG. 7 . Details are thesame as those described with reference to FIG. 7 . According toembodiments, the intra/inter-coding processor 12005 may be included inthe arithmetic coder 12006.

The arithmetic coder 12006 according to the embodiments performs entropyencoding on an octree of the point cloud data and/or an approximatedoctree. For example, the encoding scheme includes arithmetic encoding.The arithmetic coder 12006 performs an operation and/or method the sameas or similar to the operation and/or method of the arithmetic encoder40004.

The metadata processor 12007 according to the embodiments processesmetadata about the point cloud data, for example, a set value, andprovides the same to a necessary processing process such as geometryencoding and/or attribute encoding. Also, the metadata processor 12007according to the embodiments may generate and/or process signalinginformation related to the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beencoded separately from the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beinterleaved.

The color transform processor 12008, the attribute transform processor12009, the prediction/lifting/RAHT transform processor 12010, and thearithmetic coder 12011 perform the attribute encoding. The attributeencoding according to the embodiments is the same as or similar to theattribute encoding described with reference to FIGS. 1 to 9 , and thus adetailed description thereof is omitted.

The color transform processor 12008 according to the embodimentsperforms color transform coding to transform color values included inattributes. The color transform processor 12008 may perform colortransform coding based on the reconstructed geometry. The reconstructedgeometry is the same as described with reference to FIGS. 1 to 9 . Also,it performs an operation and/or method the same as or similar to theoperation and/or method of the color transformer 40006 described withreference to FIG. 4 is performed. The detailed description thereof isomitted.

The attribute transform processor 12009 according to the embodimentsperforms attribute transformation to transform the attributes based onthe reconstructed geometry and/or the positions on which geometryencoding is not performed. The attribute transform processor 12009performs an operation and/or method the same as or similar to theoperation and/or method of the attribute transformer 40007 describedwith reference to FIG. 4 . The detailed description thereof is omitted.The prediction/lifting/RAHT transform processor 12010 according to theembodiments may code the transformed attributes by any one or acombination of RAHT coding, prediction transform coding, and liftingtransform coding. The prediction/lifting/RAHT transform processor 12010performs at least one of the operations the same as or similar to theoperations of the RAHT transformer 40008, the LOD generator 40009, andthe lifting transformer 40010 described with reference to FIG. 4 . Inaddition, the prediction transform coding, the lifting transform coding,and the RAHT transform coding are the same as those described withreference to FIGS. 1 to 9 , and thus a detailed description thereof isomitted.

The arithmetic coder 12011 according to the embodiments may encode thecoded attributes based on the arithmetic coding. The arithmetic coder12011 performs an operation and/or method the same as or similar to theoperation and/or method of the arithmetic encoder 40012.

The transmission processor 12012 according to the embodiments maytransmit each bitstream containing encoded geometry and/or encodedattributes and metadata, or transmit one bitstream configured with theencoded geometry and/or the encoded attributes and the metadata. Whenthe encoded geometry and/or the encoded attributes and the metadataaccording to the embodiments are configured into one bitstream, thebitstream may include one or more sub-bitstreams. The bitstreamaccording to the embodiments may contain signaling information includinga sequence parameter set (SPS) for signaling of a sequence level, ageometry parameter set (GPS) for signaling of geometry informationcoding, an attribute parameter set (APS) for signaling of attributeinformation coding, and a tile parameter set (TPS or tile inventory) forsignaling of a tile level, and slice data. The slice data may includeinformation about one or more slices. One slice according to embodimentsmay include one geometry bitstream Geom0⁰ and one or more attributebitstreams Attr0⁰ and Attr1⁰.

The slice is a series of a syntax element representing in whole or inpart of the coded point cloud frame.

The TPS according to the embodiments may include information about eachtile (for example, coordinate information and height/size informationabout a bounding box) for one or more tiles. The geometry bitstream maycontain a header and a payload. The header of the geometry bitstreamaccording to the embodiments may contain a parameter set identifier(geom_parameter_set_id), a tile identifier (geom_tile_id) and a sliceidentifier (geom_slice_id) included in the GPS, and information aboutthe data contained in the payload. As described above, the metadataprocessor 12007 according to the embodiments may generate and/or processthe signaling information and transmit the same to the transmissionprocessor 12012. According to embodiments, the elements to performgeometry encoding and the elements to perform attribute encoding mayshare data/information with each other as indicated by dotted lines. Thetransmission processor 12012 according to the embodiments may perform anoperation and/or transmission method the same as or similar to theoperation and/or transmission method of the transmitter 10003. Detailsare the same as those described with reference to FIGS. 1 and 2 , andthus a description thereof is omitted.

FIG. 13 illustrates a reception device according to embodiments.

The reception device illustrated in FIG. 13 is an example of thereception device 10004 of FIG. 1 (or the point cloud video decoder ofFIGS. 10 and 11 ). The reception device illustrated in FIG. 13 mayperform one or more of the operations and methods the same as or similarto those of the point cloud video decoder described with reference toFIGS. 1 to 11 .

The reception device according to the embodiment includes a receiver13000, a reception processor 13001, an arithmetic decoder 13002, anoccupancy code-based octree reconstruction processor 13003, a surfacemodel processor (triangle reconstruction, up-sampling, voxelization)13004, an inverse quantization processor 13005, a metadata parser 13006,an arithmetic decoder 13007, an inverse quantization processor 13008, aprediction/lifting/RAHT inverse transform processor 13009, a colorinverse transform processor 13010, and/or a renderer 13011. Each elementfor decoding according to the embodiments may perform an inverse processof the operation of a corresponding element for encoding according tothe embodiments.

The receiver 13000 according to the embodiments receives point clouddata. The receiver 13000 may perform an operation and/or receptionmethod the same as or similar to the operation and/or reception methodof the receiver 10005 of FIG. 1 . The detailed description thereof isomitted.

The reception processor 13001 according to the embodiments may acquire ageometry bitstream and/or an attribute bitstream from the received data.The reception processor 13001 may be included in the receiver 13000.

The arithmetic decoder 13002, the occupancy code-based octreereconstruction processor 13003, the surface model processor 13004, andthe inverse quantization processor 1305 may perform geometry decoding.The geometry decoding according to embodiments is the same as or similarto the geometry decoding described with reference to FIGS. 1 to 10 , andthus a detailed description thereof is omitted.

The arithmetic decoder 13002 according to the embodiments may decode thegeometry bitstream based on arithmetic coding. The arithmetic decoder13002 performs an operation and/or coding the same as or similar to theoperation and/or coding of the arithmetic decoder 11000.

The occupancy code-based octree reconstruction processor 13003 accordingto the embodiments may reconstruct an octree by acquiring an occupancycode from the decoded geometry bitstream (or information about thegeometry secured as a result of decoding). The occupancy code-basedoctree reconstruction processor 13003 performs an operation and/ormethod the same as or similar to the operation and/or octree generationmethod of the octree synthesizer 11001. When the trisoup geometryencoding is applied, the surface model processor 1302 according to theembodiments may perform trisoup geometry decoding and related geometryreconstruction (for example, triangle reconstruction, up-sampling,voxelization) based on the surface model method. The surface modelprocessor 1302 performs an operation the same as or similar to that ofthe surface approximation synthesizer 11002 and/or the geometryreconstructor 11003.

The inverse quantization processor 1305 according to the embodiments mayinversely quantize the decoded geometry.

The metadata parser 1306 according to the embodiments may parse metadatacontained in the received point cloud data, for example, a set value.The metadata parser 1306 may pass the metadata to geometry decodingand/or attribute decoding. The metadata is the same as that describedwith reference to FIG. 12 , and thus a detailed description thereof isomitted.

The arithmetic decoder 13007, the inverse quantization processor 13008,the prediction/lifting/RAHT inverse transform processor 13009 and thecolor inverse transform processor 13010 perform attribute decoding. Theattribute decoding is the same as or similar to the attribute decodingdescribed with reference to FIGS. 1 to 10 , and thus a detaileddescription thereof is omitted.

The arithmetic decoder 13007 according to the embodiments may decode theattribute bitstream by arithmetic coding. The arithmetic decoder 13007may decode the attribute bitstream based on the reconstructed geometry.The arithmetic decoder 13007 performs an operation and/or coding thesame as or similar to the operation and/or coding of the arithmeticdecoder 11005.

The inverse quantization processor 13008 according to the embodimentsmay inversely quantize the decoded attribute bitstream. The inversequantization processor 13008 performs an operation and/or method thesame as or similar to the operation and/or inverse quantization methodof the inverse quantizer 11006.

The prediction/lifting/RAHT inverse transformer 13009 according to theembodiments may process the reconstructed geometry and the inverselyquantized attributes. The prediction/lifting/RAHT inverse transformprocessor 1301 performs one or more of operations and/or decoding thesame as or similar to the operations and/or decoding of the RAHTtransformer 11007, the LOD generator 11008, and/or the inverse lifter11009. The color inverse transform processor 13010 according to theembodiments performs inverse transform coding to inversely transformcolor values (or textures) included in the decoded attributes. The colorinverse transform processor 13010 performs an operation and/or inversetransform coding the same as or similar to the operation and/or inversetransform coding of the color inverse transformer 11010. The renderer13011 according to the embodiments may render the point cloud data.

FIG. 14 shows an exemplary structure operatively connectable with amethod/device for transmitting and receiving point cloud data accordingto embodiments.

The structure of FIG. 14 represents a configuration in which at leastone of a server 17600, a robot 17100, a self-driving vehicle 17200, anXR device 17300, a smartphone 17400, a home appliance 17500, and/or ahead-mount display (HMD) 17700 is connected to a cloud network 17100.The robot 17100, the self-driving vehicle 17200, the XR device 17300,the smartphone 17400, or the home appliance 17500 is referred to as adevice. In addition, the XR device 17300 may correspond to a point cloudcompressed data (PCC) device according to embodiments or may beoperatively connected to the PCC device.

The cloud network 17000 may represent a network that constitutes part ofthe cloud computing infrastructure or is present in the cloud computinginfrastructure. Here, the cloud network 17000 may be configured using a3G network, 4G or Long Term Evolution (LTE) network, or a 5G network.

The server 17600 may be connected to at least one of the robot 17100,the self-driving vehicle 17200, the XR device 17300, the smartphone17400, the home appliance 17500, and/or the HMD 17700 over the cloudnetwork 17000 and may assist in at least a part of the processing of theconnected devices 17100 to 17700.

The HMD 17700 represents one of the implementation types of the XRdevice and/or the PCC device according to the embodiments. The HMD typedevice according to the embodiments includes a communication unit, acontrol unit, a memory, an I/O unit, a sensor unit, and a power supplyunit.

Hereinafter, various embodiments of the devices 17100 to 17500 to whichthe above-described technology is applied will be described. The devices17100 to 17500 illustrated in FIG. 14 may be operativelyconnected/coupled to a point cloud data transmission device andreception according to the above-described embodiments.

<PCC+XR>

The XR/PCC device 17300 may employ PCC technology and/or XR (AR+VR)technology, and may be implemented as an HMD, a head-up display (HUD)provided in a vehicle, a television, a mobile phone, a smartphone, acomputer, a wearable device, a home appliance, a digital signage, avehicle, a stationary robot, or a mobile robot.

The XR/PCC device 17300 may analyze 3D point cloud data or image dataacquired through various sensors or from an external device and generateposition data and attribute data about 3D points. Thereby, the XR/PCCdevice 17300 may acquire information about the surrounding space or areal object, and render and output an XR object. For example, the XR/PCCdevice 17300 may match an XR object including auxiliary informationabout a recognized object with the recognized object and output thematched XR object.

<PCC+Self-Driving+XR>

The self-driving vehicle 17200 may be implemented as a mobile robot, avehicle, an unmanned aerial vehicle, or the like by applying the PCCtechnology and the XR technology.

The self-driving vehicle 17200 to which the XR/PCC technology is appliedmay represent a self-driving vehicle provided with means for providingan XR image, or a self-driving vehicle that is a target ofcontrol/interaction in the XR image. In particular, the self-drivingvehicle 17200 which is a target of control/interaction in the XR imagemay be distinguished from the XR device 17300 and may be operativelyconnected thereto.

The self-driving vehicle 17200 having means for providing an XR/PCCimage may acquire sensor information from sensors including a camera,and output the generated XR/PCC image based on the acquired sensorinformation. For example, the self-driving vehicle 17200 may have an HUDand output an XR/PCC image thereto, thereby providing an occupant withan XR/PCC object corresponding to a real object or an object present onthe screen.

When the XR/PCC object is output to the HUD, at least a part of theXR/PCC object may be output to overlap the real object to which theoccupant's eyes are directed. On the other hand, when the XR/PCC objectis output on a display provided inside the self-driving vehicle, atleast a part of the XR/PCC object may be output to overlap an object onthe screen. For example, the self-driving vehicle 17200 may outputXR/PCC objects corresponding to objects such as a road, another vehicle,a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian,and a building.

The virtual reality (VR) technology, the augmented reality (AR)technology, the mixed reality (MR) technology and/or the point cloudcompression (PCC) technology according to the embodiments are applicableto various devices.

In other words, the VR technology is a display technology that providesonly CG images of real-world objects, backgrounds, and the like. On theother hand, the AR technology refers to a technology that shows avirtually created CG image on the image of a real object. The MRtechnology is similar to the AR technology described above in thatvirtual objects to be shown are mixed and combined with the real world.However, the MR technology differs from the AR technology in that the ARtechnology makes a clear distinction between a real object and a virtualobject created as a CG image and uses virtual objects as complementaryobjects for real objects, whereas the MR technology treats virtualobjects as objects having equivalent characteristics as real objects.More specifically, an example of MR technology applications is ahologram service.

Recently, the VR, AR, and MR technologies are sometimes referred to asextended reality (XR) technology rather than being clearly distinguishedfrom each other. Accordingly, embodiments of the present disclosure areapplicable to any of the VR, AR, MR, and XR technologies. Theencoding/decoding based on PCC, V-PCC, and G-PCC techniques isapplicable to such technologies.

The PCC method/device according to the embodiments may be applied to avehicle that provides a self-driving service.

A vehicle that provides the self-driving service is connected to a PCCdevice for wired/wireless communication.

When the point cloud compression data (PCC) transmission/receptiondevice according to the embodiments is connected to a vehicle forwired/wireless communication, the device may receive/process contentdata related to an AR/VR/PCC service, which may be provided togetherwith the self-driving service, and transmit the same to the vehicle. Inthe case where the PCC transmission/reception device is mounted on avehicle, the PCC transmission/reception device may receive/processcontent data related to the AR/VR/PCC service according to a user inputsignal input through a user interface device and provide the same to theuser. The vehicle or the user interface device according to theembodiments may receive a user input signal. The user input signalaccording to the embodiments may include a signal indicating theself-driving service.

Meanwhile, the point cloud video encoder on the transmitting side mayfurther perform a spatial partitioning process of spatially partitioning(or dividing) the point cloud data into one or more 3D blocks beforeencoding the point cloud data. That is, in order for the encoding andtransmission operations of the transmission device and the decoding andrendering operations of the reception device to be performed in realtime and processed with low latency, the transmission device mayspatially partition the point cloud data into a plurality of regions. Inaddition, the transmission device may independently or non-independentlyencode the spatially partitioned regions (or blocks), thereby enablingrandom access and parallel encoding in the three-dimensional spaceoccupied by the point cloud data. In addition, the transmission deviceand the reception device may perform encoding and decoding independentlyor non-independently for each spatially partitioned region (or block),thereby preventing errors from being accumulated in the encoding anddecoding process.

FIG. 15 is a diagram illustrating another example of a point cloudtransmission device according to embodiments, including a spatialpartitioner.

The point cloud transmission device according to the embodiments mayinclude a data input unit 51001, a coordinates transformation unit51002, a quantization processor 51003, a spatial partitioner 51004, asignaling processor 51005, a geometry encoder 51006, an attributeencoder 51007, and a transmission processor 51008. According toembodiments, the coordinates transformation unit 51002, the quantizationprocessor 51003, the spatial partitioner 51004, the geometry encoder51006, and the attribute encoder 51007 may be referred to as point cloudvideo encoders.

The data input unit 51001 may perform some or all of the operations ofthe point cloud video acquisition unit 10001 of FIG. 1 , or may performsome or all of the operations of the data input unit 12000 of FIG. 12 .The coordinates transformation unit 51002 may perform some or all of theoperations of the coordinates transformation unit 40000 of FIG. 4 .Further, the quantization processor 51003 may perform some or all of theoperations of the quantization unit 40001 of FIG. 4 , or may performsome or all of the operations of the quantization processor 12001 ofFIG. 12 .

The spatial partitioner 51004 may spatially partition the point clouddata quantized and output from the quantization processor 51003 into oneor more 3D blocks based on a bounding box and/or a sub-bounding box.Here, the 3D block may refer to a tile group, a tile, a slice, a codingunit (CU), a prediction unit (PU), or a transform unit (TU). In oneembodiment, the signaling information for spatial partition isentropy-encoded by the signaling processor 51005 and then transmittedthrough the transmission processor 51008 in the form of a bitstream.

FIGS. 16(a) to 16(c) illustrate an embodiment of partitioning a boundingbox into one or more tiles. As shown in FIG. 16(a), a point cloudobject, which corresponds to point cloud data, may be expressed in theform of a box based on a coordinate system, which is referred to as abounding box. In other words, the bounding box represents a cube capableof containing all points of the point cloud.

FIGS. 16(b) and 16(c) illustrate an example in which the bounding box ofFIG. 16(a) is partitioned into tile 1# and tile 2#, and tile 2# ispartitioned again into slice 1# and slice 2#.

In one embodiment, the point cloud content may be one person such as anactor, multiple people, one object, or multiple objects. In a largerrange, it may be a map for autonomous driving or a map for indoornavigation of a robot. In this case, the point cloud content may be avast amount of locally connected data. In this case, the point cloudcontent cannot be encoded/decoded at once, and accordingly tilepartitioning may be performed before the point cloud content iscompressed. For example, room #101 in a building may be partitioned intoone tile and room #102 in the building may be partitioned into anothertile. In order to support fast encoding/decoding by applyingparallelization to the partitioned tiles, the tiles may be partitioned(or split) into slices again. This operation may be referred to as slicepartitioning (or splitting).

That is, a tile may represent a partial region (e.g., a rectangularcube) of a 3D space occupied by point cloud data according toembodiments. According to embodiments, a tile may include one or moreslices. The tile according to the embodiments may be partitioned intoone or more slices, and thus the point cloud video encoder may encodepoint cloud data in parallel.

A slice may represent a unit of data (or bitstream) that may beindependently encoded by the point cloud video encoder according to theembodiments and/or a unit of data (or bitstream) that may beindependently decoded by the point cloud video decoder. A slice may be aset of data in a 3D space occupied by point cloud data, or a set of somedata among the point cloud data. A slice according to the embodimentsmay represent a region or set of points included in a tile according toembodiments. According to embodiments, a tile may be partitioned intoone or more slices based on the number of points included in one tile.For example, one tile may be a set of points partitioned by the numberof points. According to embodiments, a tile may be partitioned into oneor more slices based on the number of points, and some data may be splitor merged in the partitioning process. That is, a slice may be a unitthat may be independently coded within a corresponding tile. In thisway, a tile obtained by spatially partitioning may be partitioned intoone or more slices for fast and efficient processing.

The point cloud video encoder according to the embodiments may encodepoint cloud data on a slice-by-slice basis or a tile-by-tile basis,wherein a tile includes one or more slices. In addition, the point cloudvideo encoder according to the embodiments may perform differentquantization and/or transformation for each tile or each slice.

Positions of one or more 3D blocks (e.g., slices) spatially partitionedby the spatial partitioner 51004 are output to the geometry encoder51006, and the attribute information (or attributes) is output to theattribute encoder 51007. The positions may be position information aboutthe points included in a partitioned unit (box, block, tile, tile group,or slice), and are referred to as geometry information.

The geometry encoder 51006 constructs and encodes (i.e., compresses) anoctree based on the positions output from the spatial partitioner 51004to output a geometry bitstream. The geometry encoder 51006 mayreconstruct an octree and/or an approximated octree and output the sameto the attribute encoder 51007. The reconstructed octree may be referredto as reconstructed geometry (or restored geometry).

The attribute encoder 51007 encodes (i.e., compresses) the attributesoutput from the spatial partitioner 51004 based on the reconstructedgeometry output from the geometry encoder 51006, and outputs anattribute bitstream.

FIG. 17 is a detailed block diagram illustrating another example of thegeometry encoder 51006 and the attribute encoder 51007 according toembodiments.

The voxelization processor 53001, the octree generator 53002, thegeometry information predictor 53003, and the arithmetic coder 53004 ofthe geometry encoder 51006 of FIG. 17 may perform some or all of theoperations of the octree analysis unit 40002, the surface approximationanalysis unit 40003, the arithmetic encoder 40004, and the geometryreconstruction unit 40005 of FIG. 4 , or may perform some or all of theoperations of the voxelization processor 12002, the octree occupancycode generator 12003, and the surface model processor 12004, theintra/inter-coding processor 12005, and the arithmetic coder 12006 ofFIG. 12

The attribute encoder 51007 of FIG. 17 may include a colortransformation processor 53005, an attribute transformation processor5306, a LOD configuration unit 53007, a neighbor point groupconfiguration unit 53008, an attribute information prediction unit53009, a residual attribute information quantization processor 53010,and an arithmetic coder 53011.

In an embodiment, a quantization processor may be further providedbetween the spatial partitioner 51004 and the voxelization processor53001. The quantization processor quantizes positions of one or more 3Dblocks (e.g., slices) spatially partitioned by the spatial partitioner51004. In this case, the quantization processor may perform some or allof the operations of the quantization unit 40001 of FIG. 4 , or performsome or all of the operations of the quantization processor 12001 ofFIG. 12 . When the quantization processor is further provided betweenthe spatial partitioner 51004 and the voxelization processor 53001, thequantization processor 51003 of FIG. 15 may or may not be omitted.

The voxelization processor 53001 according to the embodiments performsvoxelization based on the positions of the one or more spatiallypartitioned 3D blocks (e.g., slices) or the quantized positions thereof.Voxelization refers to the minimum unit expressing position informationin a 3D space. Points of point cloud content (or 3D point cloud video)according to embodiments may be included in one or more voxels.According to embodiments, one voxel may include one or more points. Inan embodiment, in the case where quantization is performed beforevoxelization is performed, a plurality of points may belong to onevoxel.

In the present specification, when two or more points are included inone voxel, the two or more points are referred to as duplicated points.That is, in the geometry encoding process, overlapping points may begenerated through geometry quantization and voxelization.

The voxelization processor 53001 according to the embodiments may outputthe duplicated points belonging to one voxel to the octree generator53002 without merging the same, or may merge the multiple points intoone point and output the merged point to the octree generator 53002.

The octree generator 53002 according to the embodiments generates anoctree based on a voxel output from the voxelization processor 53001.

The geometry information predictor 53003 according to the embodimentspredicts and compresses geometry information based on the octreegenerated by the octree generator 53002, and outputs the predicted andcompressed information to the arithmetic coder 53004. In addition, thegeometry information predictor 53003 reconstructs the geometry based onthe positions changed through compression, and outputs the reconstructed(or decoded) geometry to the LOD configuration unit 53007 of theattribute encoder 51007. The reconstruction of the geometry informationmay be performed in a device or component separate from the geometryinformation predictor 53003. In another embodiment, the reconstructedgeometry may also be provided to the attribute transformation processor53006 of the attribute encoder 51007.

The color transformation processor 53005 of the attribute encoder 51007corresponds to the color transformation unit 40006 of FIG. 4 or thecolor transformation processor 12008 of FIG. 12 . The colortransformation processor 53005 according to the embodiments performscolor transformation coding of transforming color values (or textures)included in the attributes provided from the data input unit 51001and/or the spatial partitioner 51004. For example, the colortransformation processor 53005 may transform the format of colorinformation (e.g., from RGB to YCbCr). The operation of the colortransformation processor 53005 according to the embodiments may beoptionally applied according to color values included in the attributes.In another embodiment, the color transformation processor 53005 mayperform color transformation coding based on the reconstructed geometry.For details of the geometry reconstruction, refer to the description ofFIGS. 1 to 9 .

The attribute transformation processor 53006 according to theembodiments may perform attribute transformation of transformingattributes based on positions on which geometry encoding has not beenperformed and/or the reconstructed geometry.

The attribute transformation processor 53006 may be referred to as arecoloring unit.

The operation of the attribute transformation processor 53006 accordingto the embodiments may be optionally applied according to whetherduplicated points are merged. According to an embodiment, merging of theduplicated points may be performed by the voxelization processor 53001or the octree generator 53002 of the geometry encoder 51006.

In the present specification, when points belonging to one voxel aremerged into one point in the voxelization processor 53001 or the octreegenerator 53002, the attribute transformation processor 53006 performsan attribute transformation. Take an example.

The attribute transformation processor 53006 performs an operationand/or method identical or similar to the operation and/or method of theattribute transformation unit 40007 of FIG. 4 or the attributetransformation processor 12009 of FIG. 12 .

According to embodiments, the geometry information reconstructed by thegeometry information predictor 53003 and the attribute informationoutput from the attribute transformation processor 53006 are provided tothe LOD configuration unit 53007 for attribute compression.

According to embodiments, the attribute information output from theattribute transformation processor 53006 may be compressed by one or acombination of two or more of RAHT coding, LOD-based predictivetransform coding, and lifting transform coding based on thereconstructed geometry information.

Hereinafter, it is assumed that attribute compression is performed byone or a combination of the LOD-based predictive transform coding andthe lifting transform coding as an embodiment. Thus, a description ofthe RAHT coding will be omitted. For details of the RAHT transformcoding, refer to the descriptions of FIGS. 1 to 9 .

The LOD configuration unit 53007 according to the embodiments generatesa Level of Detail (LOD).

The LOD is a degree representing the detail of the point cloud content.As the LOD value decreases, it indicates that the detail of the pointcloud content is degraded. As the LOD value increases, it indicates thatthe detail of the point cloud content is enhanced. Points may beclassified according to the LOD.

In an embodiment, in the predictive transform coding and the liftingtransform coding, points may be divided into LODs and grouped.

This operation may be referred to as an LOD generation process, and agroup having different LODs may be referred to as an LOD₁ set. Here, 1denotes the LOD and is an integer starting from 0. LOD₀ is a setconsisting of points with the largest distance therebetween. As 1increases, the distance between points belonging to LOD₁ decreases.

When the LOD₁ set is generated by the LOD configuration unit 53007, theneighbor point configuration unit 53008 according to the embodiment mayfind X (>0) nearest neighbor points in a group having the same or lowerLOD (i.e., a large distance between nodes) based on the LOD₁ set andregister the same in the predictor as a neighbor set. X is the maximumnumber of points that may be set as neighbors. X may be input as a userparameter, or be signaled in signaling information through the signalingprocessor 51005 (e.g., the lifting_num_pred_nearest_neighbours fieldsignaled in the APS).

Referring to FIG. 9 as an example, neighbors of P3 belonging to LOD₁ arefound in LOD₀ and LOD₁. For example, when the maximum number (X) ofpoints that may be set as neighbors is 3, three nearest neighbor nodesof P3 may be P2 P4 P6. These three nodes are registered as in thepredictor of P3 as a neighbor set. In an embodiment, among theregistered nodes, the neighbor node P4 is the closest to P3 in terms ofdistance, then P6 is the next nearest node, and P2 is the farthest nodeamong the three nodes. Here, X=3 is merely an embodiment configured toprovide understanting of the present disclosure. The value of X mayvary.

As described above, all points of the point cloud data may have apredictor.

The attribute information prediction unit 53009 according to theembodiments predicts an attribute value based on the neighbor pointsregistered in the predictor. The predictor may have a registeredneighbor set and register a “½ distance=weight” based on the distancefrom each neighbor point. For example, the predictor of node P3 has (P2P4 P6) as a neighbor set and calculates the weight based on the distancefrom each neighbor point. According to embodiments, the weights of therespective neighbor points may be (1/√(P2−P3)², 1/√(P4−P3)²,1/√(P6−P3)²).

According to embodiments, when a neighbor set of the predictor is set,the neighbor point configuration unit 53008 or the attribute informationprediction unit 53009 may normalize the weights of the respectiveneighbor points by the total sum of the weights of the neighbor points.

For example, the weights of all neighbor points in the neighbor set ofnode P3 are added (total_weight=1/√(P2−P3)²+1/√(P4−P3)²+1/√(P6−P3)²),and the weight of each neighbor point is divided by the sum of theweights (1/√(P2−P3)²/total_weight, 1/√(P4−P3)²/total_weight,1/√(P6−P3)²/total_weight). Thereby, the weight of each neighbor point isnormalized

Then, the attribute information prediction unit 5301 may predict anattribute value through the predictor.

According to embodiments, the average of the values obtained bymultiplying the attributes (e.g., color, reflectance, etc.) of neighborpoints registered in the predictor by a weight (or a normalized weight)may be set as a predicted result (i.e., a predicted attribute value).Alternatively, an attribute of a specific point may be set as apredicted result (i.e., a predicted attribute value). According toembodiments, the predicted attribute value may be referred to aspredicted attribute information. In addition, a residual attribute value(or residual attribute information or residual) may be obtainedsubtracting the predicted attribute value (or predicted attributeinformation) of the point from the attribute value (i.e., the originalattribute value) of the point.

According to embodiments, compressed result values may be pre-calculatedby applying various prediction modes (or predictor indexes), and then aprediction mode (i.e., a predictor index) generating the smallestbitstream may be selected from among the prediction modes.

Next, a process of selecting a prediction mode will be described indetail.

In this specification, a prediction mode has the same meaning as apredictor index (Preindex), and may be broadly referred to as aprediction method.

In an embodiment, the process of finding the most suitable predictionmode for each point and setting the found prediction mode in thepredictor of the corresponding point may be performed by the attributeinformation prediction unit 53009.

According to embodiments, a prediction mode in which a predictedattribute value is calculated through a weighted average (i.e., anaverage obtained by multiplying the attributes of neighbor points set inthe predictor of each point by a weight calculated based on the distanceto each neighbor point) will be referred to as prediction mode 0. Inaddition, a prediction mode in which the attribute of the first neighborpoint is set as a predicted attribute value will be referred to asprediction mode 1, a prediction mode in which the attribute of thesecond neighbor point is set as a predicted attribute value will bereferred to as prediction mode 2, and a prediction mode in which theattribute of the third neighbor point is set as a predicted attributevalue will be referred to as prediction mode 3. In other words, thevalue of the prediction mode (or predictor index) equal to 0 mayindicate that the attribute value is predicted through the weightedaverage, and the value equal to 1 may indicate that the attribute valueis predicted through the first neighbor node (i.e., the neighbor point).The value equal to 2 may indicate that the attribute value is predictedthrough the second neighbor node, and the value equal to 3 may indicatethat the attribute value is predicted through the third neighboringnode.

According to embodiments, a residual attribute value in prediction mode0, a residual attribute value in prediction mode 1, a residual attributevalue in prediction mode 2, and a residual attribute value in predictionmode 3 may be calculated, and a score or double score may be calculatedbased on each residual attribute value. Then, a prediction mode havingthe least calculated score may be selected and set as a prediction modeof the corresponding point. The method of calculating scores will bedescribed in detail later.

For example, when the score calculated based on the residual attributevalue obtained by setting the attribute of the second neighbor point asthe predicted attribute value is the least value, the predictor indexvalue is 2, that is, prediction mode 2 is selected as the predictionmode of the point.

As in the example above, when it is assumed that neighbor point P4 amongneighbor points P2, P4, and P6 of P3 is the closest to point P3 in termsof distance, neighbor point P6 is the second nearest point, and neighborpoint P2 is the farthest point, the first neighbor point may be P4, thesecond neighbor point may be P6, and the third neighbor point may be P2.

This is an exemplary embodiment for better understanding of the presentdisclosure, and the values allocated to the prediction modes, the numberof candidate prediction modes, and prediction methods may be added,deleted, or changed by those skilled in the art. Accordingly, thepresent disclosure is not limited to the above-described embodiments.

According to embodiments, the process of finding the most suitableprediction mode among a plurality of prediction modes and setting thesame as a prediction mode of a corresponding point may be performed whena preset condition is satisfied. Accordingly, when the preset conditionis not satisfied, a fixed prediction mode, e.g., prediction mode 0, inwhich a prediction attribute value is calculated through a weightedaverage, may be set as the prediction mode of the point withoutperforming the process of finding the most suitable prediction mode. Inan embodiment, this process is performed for each point.

According to embodiments, the preset condition may be satisfied for aspecific point when the difference in attribute elements (e.g., R, G, B)between neighbor points registered in the predictor of a point isgreater than or equal to a preset threshold (e.g.,lifting_adaptive_prediction_threshold), or when the differences inattribute elements (e.g., R, G, B) between the neighbor pointsregistered in the predictor of the point are calculated, and the sum ofthe largest differences for the elements is greater than or equal to apreset threshold. For example, suppose that point P3 is the specificpoint, and points P2, P4, and P6 are registered as neighbor points ofpoint P3. Additionally, suppose that, when differences in R, G, and Bvalues between points P2 and P4, differences in R, G, and B valuesbetween points P2 and P6, and differences in R, G, and B values betweenpoints P4 and P6 are calculated, the largest difference in R value isobtained between points P2 and P4, the largest difference in G value isobtained between points P4 and P6, and the largest difference in B valueis obtained between points P2 and P6. In addition, suppose that thedifference in R value between points P2 and P4 is the largest among thelargest difference in R value (between P2 and P4) and the largestdifference in G value (between P4 and P6), and the largest difference inB value (between P2 and P6).

On these assumptions, when the difference in R value between points P2and P4 is greater than or equal to a preset threshold, or when the sumof the difference in R value between points P2 and P4, the difference inG value between points P4 and P6, and the difference in B value betweenpoints P2 and P6 is greater than or equal to a preset threshold, aprocess of finding the most suitable prediction mode among a pluralityof candidate prediction modes may be performed. In addition, theprediction mode (e.g., predIndex) may be signaled only when thedifference in R value between points P2 and P4 is greater than or equalto a preset threshold, or the sum of the difference in R value betweenpoints P2 and P4, the difference in G value between points P4 and P6,and the difference in B value between points P2 and P6 is greater thanor equal to a preset threshold.

According to another embodiment, the preset condition may be satisfiedfor a specific point when the maximum difference between values of anattribute (e.g., reflectance) of neighbor points registered in thepredictor of the point is greater than or equal to a preset threshold(e.g., lifting_adaptive_prediction_threshold). For example, suppose thatthe difference in reflectance between points P2 and P4 is the largestamong the difference in reflectance between points P2 and P4, thedifference in reflectance between points P2 and P6, and the differencein reflectance between points P4 and P6.

On this assumption, the process of finding the most suitable predictionmode among a plurality of candidate prediction modes may be performedwhen the difference in reflectance between points P2 and P4 is greaterthan or equal to a preset threshold. In addition, the prediction mode(e.g., predIndex) may be signaled only when the difference inreflectance between points P2 and P4 is greater than or equal to thepreset threshold.

According to embodiments, the prediction mode (e.g., predIndex) may besignaled in attribute slice data. In another embodiment, when theprediction mode is not signaled, the transmitting side may calculate apredicted attribute value based on a prediction mode (e.g., predictionmode 0) set as a default mode, and calculate and transmit a residualattribute value based on the difference between the original attributevalue and the predicted attribute value. The receiving side maycalculate a predicted attribute value based on a prediction mode (e.g.,prediction mode 0) set as a default mode, and restore the attributevalue by adding the value to the received residual attribute value.

According to embodiments, the threshold may be directly input orsignaled in the signaling information through the signaling processor51005 (e.g., the lifting_adaptive_prediction_threshold field signaled inthe APS).

According to embodiments, when a preset condition is satisfied for aspecific point as described above, predictor candidates may begenerated. The predictor candidates are referred to as prediction modesor predictor indexes.

According to embodiments, prediction modes 1 to 3 may be included in thepredictor candidates. According to embodiments, prediction mode 0 may ormay not be included in the predictor candidates. According toembodiments, at least one prediction mode not mentioned above may befurther included in the predictor candidates.

FIG. 18 is a diagram illustrating an embodiment of the method ofselecting a prediction mode for attribute encoding according toembodiments. According to an embodiment, the operations of FIG. 18 maybe performed by the attribute information prediction unit 53009.

FIG. 18 shows an embodiment of finding the most suitable prediction modeof attributes for an i-th point based on prediction modes 0 to 3.

That is, the maximum difference between the attribute values of theneighbor points registered in the predictor of the i-th point iscalculated (step 55001), and it is determined whether the maximumdifference calculated in step 55001 is greater than or equal to a presetthreshold (step 55002).

When it is determined in step 55002 according to the embodiments thatthe maximum difference is less than the preset threshold, a presetdefault prediction mode is set as the prediction mode of the point (step55003). In an embodiment, the preset default prediction mode isprediction mode 0 (i.e., prediction of the attribute value based on aweighted average). The preset default prediction mode may be aprediction mode different from prediction mode 0. According toembodiments, when the preset default prediction mode is set as theprediction mode of the point, the prediction mode of the point is notsignaled. In this case, it is assumed that the preset default predictionmode is pre-known to the transmitting side/receiving side.

When it is determined in step 55002 that the maximum difference isgreater than or equal to the preset threshold according to embodiments,predictor candidates are generated (step 55004).

According to an embodiment, the number of predictor candidates generatedin step S5004 is greater than 1. According to embodiments, the predictorcandidates generated in step 55004 are predictor candidatescorresponding to prediction modes 0 to 3.

When predictor candidates are generated in step 55004, a process ofselecting an optimal predictor is performed by applying arate-distortion optimization (RDO) procedure.

To this end, when four predictor candidates corresponding to predictionmodes 0 to 3 are generated in step 55004, a score or double score ofeach predictor is calculated (step 55005).

According to embodiments, one of the four predictor candidates is set asa base predictor, and the scores of the base predictor and the otherpredictors are calculated. According to an embodiment, a predictorcorresponding to prediction mode 0 is set as the base predictor.

The score of the base predictor is calculated as in Equation 5 below.double score=attrResidualQuant+kAttrPredLambdaR*(quant.stepSize()>>kFixedPointAttributeShift)  Equation 5

The scores of the other predictors are calculated as in Equation 6below.double score=attrResidualQuant+idxBits*kAttrPredLambdaR*(quant.stepSize()>>kFixedPointAttributeShift)  Equation 6

Here, idxBits=i+(i==lifting_max_num_direct_predictors−1?1:2).

In Equations 5 and 6, attrResidualQuant denotes a quantized value of theresidual attribute value of the attribute, and kAttrPredLambdaR denotesa constant, which is 0.01 according to an embodiment. In addition, i(=0, 1, 2, . . . ) denotes the sequential position of a neighbor point(i.e., node), and quant.stepSize denotes the attribute quantizationsize. kFixedPointAttributeShift is a constant. According to anembodiment, kFixedPointAttributeShift is 8. In this case,“quant.stepSize( )>>kFixedPointAttributeShift” means shifting theattribute quantization size to the right by 8 bits.lifting_max_num_direct_predictors denotes the maximum number ofpredictors to be used for prediction. lifting_max_num_direct_predictorsaccording to the embodiments is set to 3 by default, and may be input asa user parameter or signaled in signaling information through thesignaling processor 51005.

The difference between Equation 5 and Equation 6 is that idxBits is notapplied in the score calculation of the base predictor.

A predictor candidate having the lowest score among the scores of thefour predictor candidates obtained by applying Equations 5 and 6 isfound (step 55006). Then, the prediction mode corresponding to thepredictor candidate having the lowest score is set as the predictionmode of the point (step 55007). That is, one of prediction modes 0 to 3is set as the prediction mode of the point (step 55008). For example,when the score obtained by applying the first neighbor point is thelowest score, prediction mode 1 (i.e., predictor index of 1) is set asthe prediction mode of the point.

According to embodiments, the prediction mode (e.g., predIndex) of thepoint set in step 55007 may be signaled in attribute slice data.

When a predictor candidate (i.e., a prediction mode) is selected among aplurality of predictor candidates (i.e., a plurality of candidateprediction modes) by applying Equations 5 and 6, the base predictorcorresponding to prediction mode 0 is very likely to be selected becauseidxBits is not used in calculating the score of the base predictorcorresponding to prediction mode 0.

In this case, when the maximum difference between the attribute valuesof the neighbor points registered in the predictor of the point isgreater than or equal to a preset threshold, this means that thedifference in attribute between the neighbor points is large. However,when prediction mode 0 is selected as the prediction mode of the point,a predicted attribute value is obtained as the average of the valuesobtained by multiplying the attributes of the neighbor points by weights(that is, the average of the values obtained by multiplying theattributes of the neighbor points set in the predictor of each point bya weight calculated based on the distance to each neighbor point).Accordingly, the bitstream size of the residual attribute value, whichis the difference between the original attribute value and the predictedattribute value, may be larger than that obtained from the registeredneighbor points.

In this way, the predicted attribute value is affected by a predictorcandidate selected from among the plurality of predictor candidates, andaffects the residual attribute value.

According to another embodiment of the present disclosure, in order toincrease attribute efficiency, the most suitable predictor candidate maybe selected from among the other predictor candidates excluding thepredictor corresponding to prediction mode 0.

For example, when the maximum difference between attribute values of theneighbor points registered in the predictor of the point is greater thanor equal to a preset threshold, a prediction mode having the lowestscore among the three predictor candidates, that is, prediction modes 1to 3 (i.e., the prediction modes excluding prediction mode 0), may beset as the prediction mode of the point. When the maximum difference isless than the preset threshold, prediction mode 0 may be set as theprediction mode of the point. Prediction mode 0 is a prediction mode inwhich a predicted attribute value is calculated through a weightedaverage (i.e., the average of values obtained by multiplying theattributes of the neighbor points set in the predictor of each point bya weight calculated based on the distance to each neighbor point).

FIG. 19 is a diagram illustrating another embodiment of the method ofselecting a prediction mode for attribute encoding according toembodiments. According to an embodiment, the operations of FIG. 19 maybe performed by the attribute information prediction unit 53009.

FIG. 18 shows an embodiment of finding the most suitable prediction modeof attributes for an i-th point based on prediction modes 1 to 3.

That is, the maximum difference between the attribute values of theneighbor points registered in the predictor of the point is calculated(step 57001), and it is determined whether the maximum differencecalculated in step 57001 is greater than or equal to a preset threshold(step 57002).

When it is determined in step 57002 according to the embodiments thatthe maximum difference is less than the preset threshold, a presetdefault prediction mode is set as the prediction mode of the point (step57003). In an embodiment, the preset default prediction mode isprediction mode 0 (i.e., prediction of the attribute value based on aweighted average). The preset default prediction mode may be aprediction mode different from prediction mode 0. According toembodiments, when the preset default prediction mode is set as theprediction mode of the point, the prediction mode of the point is notsignaled. In this case, it is assumed that the preset default predictionmode is pre-known to the transmitting side/receiving side.

When it is determined in step 57002 that the maximum difference isgreater than or equal to the preset threshold according to embodiments,predictor candidates are generated (step 57004).

According to an embodiment, the number of predictor candidates generatedin step S7004 is greater than 1. According to embodiments, the predictorcandidates generated in step 57004 are predictor candidatescorresponding to prediction modes 1 to 3. That is, a predictorcorresponding to prediction mode 0 is not included in the predictorcandidates. In other words, the predictor candidates correspond to theprediction modes in which attribute values of the neighbor pointsregistered in the point are set as predicted attribute values.

When predictor candidates are generated in step 57004, a process ofselecting an optimal predictor is performed by applying arate-distortion optimization (RDO) procedure.

To this end, when three predictor candidates corresponding to predictionmodes 1 to 3 are generated in step 57004, a score or double score ofeach predictor is calculated (step 57005).

The score of each predictor may be calculated as in Equation 7 below.double score=attrResidualQuant+kAttrPredLambdaR*(quant.stepSize()>>kFixedPointAttributeShift)  Equation 7

In Equation 7, attrResidualQuant denotes a quantized value of theresidual attribute value of the attribute, and kAttrPredLambdaR denotesa constant, which is 0.01 according to an embodiment. In addition,quant.stepSize denotes the attribute quantization size.kFixedPointAttributeShift is a constant. According to an embodiment,kFixedPointAttributeShift is 8. In this case, “quant.stepSize()>>kFixedPointAttributeShift” means shifting the attribute quantizationsize to the right by 8 bits.

According to embodiments, in Equation 7, at least one ofkAttrPredLambdaR, quant.stepSize( ) or kFixedPointAttributeShift may beomitted.

In another embodiment, the score of each predictor may be calculated byapplying Equation 6.

In an embodiment of the present disclosure, a predictor candidate havingthe lowest score among the scores of the three predictor candidatesobtained by applying Equation 7 is found (step 57006). Then, theprediction mode corresponding to the predictor candidate having thelowest score is set as the prediction mode of the point (step 57007).That is, one of prediction modes 1 to 3 is set as the prediction mode ofthe point (step 57008). For example, when the score obtained by applyingthe first neighbor point is the lowest score, prediction mode 1 (i.e.,predictor index of 1) is set as the prediction mode of the point.

According to embodiments, the prediction mode (e.g., predIndex) of thepoint set in step 57007 may be signaled in attribute slice data.

In this way, when the difference in attribute elements between theneighbor points registered in the point is greater than or equal to thethreshold, prediction mode 0, in which the predicted attribute value iscalculated through the weighted average, is not allowed to be used asthe prediction mode of the point. Thereby, the size of the residualattribute value of the point may be reduced.

In another embodiment, when three predictor candidates corresponding toprediction modes 1 to 3 are generated in step 57004, one of the threepredictor candidates may be set as a base predictor. Then, Equation 5may be used for the base predictor, the scores of the other twopredictors may be calculated using Equation 6. Then, a prediction modecorresponding to the predictor candidate having the lowest score amongthe scores of the three predictor candidates may be set as theprediction mode of the point, and the set prediction mode (e.g.,predIndex) may be signaled in the attribute slice data.

According to embodiments, similar attribute values of the neighborpoints registered in the point to the attribute value of the point maybe estimated, a neighbor point having the best similar attribute valuemay be selected from among the neighbor points registered in the point,and a predictor candidate selecting the neighbor point may be set as abase predictor.

In order to verify the attribute similarity according to embodiments,Euclidean color distance, correlated color temperature, or the distancemetric CIE94 defined by the Commission on Illumination (CIE) may beselectively used.

The Euclidean color distance according to the embodiments may beestimated in Equation 8 below.color distance=√{square root over((R2−R1)²+(G2−G1)²+(B2−B1)²)}  Equation 8

For the CCT according to the embodiments, RGB values may be changed toCIE (XYZ) values to be normalized as chromatic values, and then a CCTvalue may be calculated, as shown in Equation 9 below.

$\begin{matrix}{{Equation}\mspace{14mu} 9} & \; \\{{X = {{\left( {{- {0.1}}4282} \right)R} + {\left( {{1.5}4924} \right)G} + {\left( {{- {0.9}}5641} \right)B}}}{Y = {{{\left( {{- {0.3}}2466} \right)R} + {\left( {{1.5}7837} \right)G} + {\left( {{- {0.7}}3191} \right)B}} = {illunimance}}}{Z = {{\left( {{- {0.6}}8202} \right)R} + {\left( {{0.7}7073} \right)G} + {\left( {{0.5}6332} \right)B}}}{x = {{\frac{X}{\left( {X + Y + Z} \right)}y} = \frac{Y}{\left( {X + Y + Z} \right)}}}{{CCT} = {{449n^{3}} + {3525n^{2}} + {682{3.3}n} + {552{0.3}3}}}{n = \frac{\left( {x - {{0.3}320}} \right)}{\left( {{{0.1}858} - y} \right)}}} & \;\end{matrix}$

CIE94 according to the embodiments may be estimated in Equation 10below.

$\begin{matrix}{{Equation}\mspace{14mu} 10} & \; \\{{{E\; 94} = \sqrt{\left( \frac{\Delta\; L}{K_{L}S_{L}} \right)^{2} + \left( \frac{\Delta\;{Cab}}{K_{C}S_{C}} \right)^{2} + \left( \frac{\Delta\;{Hab}}{K_{L}S_{L}} \right)^{2}}}{{\Delta\; L} = {L_{1} - L_{2}}}{{{C\; 1} = {\sqrt{a\; 1^{2}} + {b\; 1^{2}}}},\ {{C\; 2} = {\sqrt{a\; 2^{2}} + {b\; 2^{2}}}},{{Cab} = {{C\; 1} - {C\; 2}}}}{{\Delta H_{ab}} = {\sqrt{{\Delta\; E_{ab}^{2}} - {\Delta L^{2}} - {\Delta\; C_{ab}^{2}}} = \sqrt{{\Delta a^{2}} - {\Delta b^{2}} - {\Delta\; C_{ab}^{2}}}}}{{{\Delta\; a} = {{a\; 1} - {a\; 2}}},\ {{\Delta\; b} = {{b\; 1} - {b\; 2}}}}{{S_{L} = 1},{{Sc} = {1 + {K_{1}C_{1}}}},{{SH} = {1 + {K_{2}C_{1}}}}}} & \;\end{matrix}$

In this way, a neighbor point having the best similar attribute valuemay be selected from among the similar attribute values of the neighborpoints registered in the point to the attribute value of the point, anda predictor candidate corresponding to the selected neighbor point maybe set as a base predictor.

According to embodiments, predictor selection-related option informationused in verifying attribute similarity may be signaled in signalinginformation. The predictor selection-related option informationaccording to the embodiments may include a attribute similarity checkmethod (attribute_similarity_check_method_type) and a base predictorselection method (attribute_base_predictor selection_type). Thesignaling information according to embodiments may be at least one of asequence parameter set, an attribute parameter set, a tile parameterset, or an attribute slice header.

In this way, when the difference in attribute elements between theneighbor points registered in the point is greater than or equal to athreshold, a predictor candidate corresponding to a neighbor pointhaving the best similar attribute value among the similar attributevalues of the neighbor points registered in the point to the attributevalue of the point may be set as a base predictor. Thereby, the size ofthe residual attribute value of the point may be reduced.

The prediction mode set for each point through the above-describedprocess and the residual attribute value in the set prediction mode areoutput to the residual attribute information quantization processor53010.

According to embodiments, the residual attribute informationquantization processor 53010 may apply zero run-length coding to theinput residual attribute values.

According to an embodiment of the present disclosure, quantization andzero run-length coding may be performed on the residual attributevalues.

According to embodiments, the arithmetic coder 53011 applies arithmeticcoding to the prediction modes and residual attribute values output fromthe residual attribute information quantization processor 53010 andoutputs the result as an attribute bitstream.

The geometry bitstream compressed and output by the geometry encoder51006 and the attribute bitstream compressed and output by the attributeencoder 51007 are output to the transmission processor 51008.

The transmission processor 51008 according to the embodiments mayperform an operation and/or transmission method identical or similar tothe operation and/or transmission method of the transmission processor12012 of FIG. 12 , and perform an operation and/or transmission methodidentical or similar to the operation and/or transmission method of thetransmitter 10003 of FIG. 1 . For details, reference will be made to thedescription of FIG. 1 or 12 .

The transmission processor 51008 according to the embodiments maytransmit the geometry bitstream output from the geometry encoder 51006,the attribute bitstream output from the attribute encoder 51007, and thesignaling bitstream output from the signaling processor 51005,respectively, or may multiplex the bitstreams into one bitstream to betransmitted.

The transmission processor 51008 according to the embodiments mayencapsulate the bitstream into a file or segment (e.g., a streamingsegment) and then transmit the encapsulated bitstream over variousnetworks such as a broadcasting network and/or a broadband network.

The signaling processor 51005 according to the embodiments may generateand/or process signaling information and output the same to thetransmission processor 51008 in the form of a bitstream. The signalinginformation generated and/or processed by the signaling processor 51005will be provided to the geometry encoder 51006, the attribute encoder51007, and/or the transmission processor 51008 for geometry encoding,attribute encoding, and transmission processing. Alternatively, thesignaling processor 51005 may receive signaling information generated bythe geometry encoder 51006, the attribute encoder 51007, and/or thetransmission processor 51008.

In the present disclosure, the signaling information may be signaled andtransmitted on a per parameter set (sequence parameter set (SPS),geometry parameter set (GPS), attribute parameter set (APS), tileparameter set (TPS), or the like) basis. Also, it may be signaled andtransmitted on the basis of a coding unit of each image, such as sliceor tile. In the present disclosure, signaling information may includemetadata (e.g., set values) related to point cloud data, and may beprovided to the geometry encoder 51006, the attribute encoder 51007,and/or the transmission processor 51008 for geometry encoding, attributeencoding, and transmission processing. Depending on the application, thesignaling information may also be defined at the system side, such as afile format, dynamic adaptive streaming over HTTP (DASH), or MPEG mediatransport (MMT), or at the wired interface side, such as high definitionmultimedia interface (HDMI), Display Port, Video Electronics StandardsAssociation (VESA), or CTA.

A method/device according to the embodiments may signal relatedinformation to add/perform an operation of the embodiments. Thesignaling information according to the embodiments may be used in atransmission device and/or a reception device.

According to an embodiment of the present disclosure, information on themaximum number of predictors to be used for attribute prediction(lifting_max_num_direct_predictors), threshold information for enablingadaptive prediction of an attribute(lifting_adaptive_prediction_threshold), option information related topredictor selection, for example, a base predictor selection method(attribute_base_predictor selection_type), an attribute similarity checkmethod (attribute_similarity_check_method_type), and the like may besignaled in at least one of a sequence parameter set, an attributeparameter set, a tile parameter set, or an attribute slice header. Inaddition, according to an embodiment, predictor index information(predIndex) indicating a prediction mode corresponding to a predictorcandidate selected from among a plurality of predictor candidates may besignaled in attribute slice data.

Similar to the point cloud video encoder of the transmission devicedescribed above, the point cloud video decoder of the reception deviceperforms the same or similar process of generating an LOD₁ set, findingthe nearest neighbor points based on the LOD₁ set and registering thesame in the predictor as a neighbor point set, calculating a weightbased on a distance from each neighbor point, and performingnormalization with the weight. Then, the decoder decodes a receivedprediction mode, and predicts an attribute value of the point accordingto the decoded prediction mode. In addition, after the received residualattribute value is decoded, the decoded residual attribute value may beadded to the predicted attribute value to restore the attribute value ofthe point.

FIG. 20 is a diagram showing another exemplary point cloud receptiondevice according to embodiments.

The point cloud reception device according to the embodiments mayinclude a reception processor 61001, a signaling processor 61002, ageometry decoder 61003, an attribute decoder 61004, and a post-processor61005. According to embodiments, the geometry decoder 61003 and theattribute decoder 61004 may be referred to as a point cloud videodecoder. According to embodiments, the point cloud video decoder may bereferred to as a PCC decoder, a PCC decoding unit, a point clouddecoder, a point cloud decoding unit, or the like.

The reception processor 61001 according to the embodiments may receive asingle bitstream, or may receive a geometry bitstream, an attributebitstream, and a signaling bitstream, respectively. When a file and/orsegment is received, the reception processor 61001 according to theembodiments may decapsulate the received file and/or segment and outputthe decapsulated file and/or segment as a bitstream.

When the single bitstream is received (or decapsulated), the receptionprocessor 61001 according to the embodiments may demultiplex thegeometry bitstream, the attribute bitstream, and/or the signalingbitstream from the single bitstream. The reception processor 61001 mayoutput the demultiplexed signaling bitstream to the signaling processor61002, the geometry bitstream to the geometry decoder 61003, and theattribute bitstream to the attribute decoder 61004.

When the geometry bitstream, the attribute bitstream, and/or thesignaling bitstream are received (or decapsulated), respectively, thereception processor 61001 according to the embodiments may deliver thesignaling bitstream to the signaling processor 61002, the geometrybitstream to the geometry decoder 61003, and the attribute bitstream tothe attribute decoder 61004.

The signaling processor 61002 may parse signaling information, forexample, information contained in the SPS, GPS, APS, TPS, metadata, orthe like from the input signaling bitstream, process the parsedinformation, and provide the processed information to the geometrydecoder 61003, the attribute decoder 61004, and the post-processor61005. In another embodiment, signaling information contained in thegeometry slice header and/or the attribute slice header may also beparsed by the signaling processor 61002 before decoding of thecorresponding slice data. That is, when the point cloud data ispartitioned into tiles and/or slices at the transmitting side as shownin FIG. 16 , the TPS includes the number of slices included in eachtile, and accordingly the point cloud video decoder according to theembodiments may check the number of slices and quickly parse theinformation for parallel decoding.

Accordingly, the point cloud video decoder according to the presentdisclosure may quickly parse a bitstream containing point cloud data asit receives an SPS having a reduced amount of data. The reception devicemay decode tiles upon receiving the tiles, and may decode each slicebased on the GPS and APS included in each tile. Thereby, decodingefficiency may be maximized.

That is, the geometry decoder 61003 may reconstruct the geometry byperforming the reverse process of the operation of the geometry encoder51006 of FIG. 15 on the compressed geometry bitstream based on signalinginformation (e.g., geometry related parameters). The geometry restored(or reconstructed) by the geometry decoder 61003 is provided to theattribute decoder 61004. The attribute decoder 61004 may restore theattribute by performing the reverse process of the operation of theattribute encoder 51007 of FIG. 15 on the compressed attribute bitstreambased on signaling information (e.g., attribute related parameters) andthe reconstructed geometry. According to embodiments, when the pointcloud data is partitioned into tiles and/or slices at the transmittingside as shown in FIG. 16 , the geometry decoder 61003 and the attributedecoder 61004 perform geometry decoding and attribute decoding on atile-by-tile basis and/or slice-by-slice basis.

FIG. 21 is a detailed block diagram illustrating another example of thegeometry decoder 61003 and the attribute decoder 61004 according toembodiments.

The arithmetic decoder 63001, the octree reconstruction unit 63002, thegeometry information prediction unit 63003, the inverse quantizationprocessor 63004, and the coordinates inverse transformation unit 63005included in the geometry decoder 61003 of FIG. 21 may perform some orall of the operations of the arithmetic decoder 11000, the octreesynthesis unit 11001, the surface approximation synthesis unit 11002,the geometry reconstruction unit 11003, and the coordinates inversetransformation unit 11004 of FIG. 11 , or may perform some or all of theoperations of the arithmetic decoder 13002, the occupancy code-basedoctree reconstruction processor 13003, the surface model processor13004, and the inverse quantization processor 13005 of FIG. 13 . Thepositions restored by the geometry decoder 61003 are output to thepost-processor 61005.

According to embodiments, when information on the maximum number ofpredictors to be used for attribute prediction(lifting_max_num_direct_predictors), threshold information for enablingadaptive prediction of an attribute(lifting_adaptive_prediction_threshold), option information related topredictor selection, for example, a base predictor selection method(attribute_base_predictor selection_type), an attribute similarity checkmethod (attribute_similarity_check_method_type), and the like aresignaled in at least one of a sequence parameter set (SPS), an attributeparameter set (APS), a tile parameter set (TPS), or an attribute sliceheader, they may be acquired and provided to the attribute decoder 61004by the signaling processor 61002, or may be acquired directly by theattribute decoder 61004.

The attribute decoder 61004 according to the embodiments may include anarrismatic decoder 63006, an LOD configuration unit 63007, a neighborpoint configuration unit 63008, an attribute information prediction unit63009, and a residual attribute information inverse quantizationprocessor 63010, and an inverse color transformation processor 63011.

The arithmetic decoder 63006 according to the embodiments mayarithmetically decode the input attribute bitstream. The arithmeticdecoder 63006 may decode the attribute bitstream based on thereconstructed geometry. The arithmetic decoder 63006 performs anoperation and/or decoding identical or similar to the operation and/ordecoding of the arithmetic decoder 11005 of FIG. 11 or the arithmeticdecoder 13007 of FIG. 13 .

According to embodiments, the attribute bitstream output from thearithmetic decoder 63006 may be decoded by one or a combination of twoor more of RAHT decoding, LOD-based predictive transform decoding, andlifting transform decoding based on the reconstructed geometryinformation.

It has been described as an embodiment that the transmission deviceperforms attribute compression by one or a combination of the LOD-basedpredictive transform coding and the lifting transform coding.Accordingly, an embodiment in which the reception device performsattribute decoding by one or a combination of the LOD-based predictivetransform decoding and the lifting transform decoding will be described.The description of the RAHT decoding for the reception device will beomitted.

According to an embodiment, an attribute bitstream that isarithmetically decoded by the arithmetic decoder 63006 is provided tothe LOD configuration unit 63007. According to embodiments, theattribute bitstream provided from the arithmetic decoder 63006 to theLOD configuration unit 63007 may contain prediction modes and residualattribute values.

The LOD configuration unit 63007 according to the embodiments generatesLODs in the same or similar manner as the LOD configuration unit 53007of the transmission device, and outputs the generated LODs to theneighbor point configuration unit 63008.

According to embodiments, the LOD configuration unit 63007 divides andgroups points into LODs. Here, a group having different LODs is referredto as an LOD₁ set. Here, 1 denotes the LOD and is an integer startingfrom 0. LOD₀ is a set consisting of points with the largest distancetherebetween. As 1 increases, the distance between points belonging toLOD₁ decreases.

According to embodiments, the prediction modes and residual attributevalues encoded by the transmission device may be provided for each LODor only for a leaf node.

In one embodiment, when the LOD₁ set is generated by the LODconfiguration unit 63007, the neighbor point configuration unit 63008may find X (>0) nearest neighbor points in a group having the same orlower LOD (i.e., a large distance between nodes) based on the LOD₁ setand register the same in the predictor as a neighbor set. Here, X is themaximum number of points that may be set as neighbors (e.g.,lifting_num_pred_nearest_neighbours). X may be input as a userparameter, or be received in signaling information such as SPS, APS,GPS, TPS, geometry slice header, or attribute slice header.

In another embodiment, the neighbor point configuration unit 63008 mayselect neighbor points of each point based on signaling information suchas SPS, APS, GPS, TPS, geometry slice header, or attribute slice header.To this end, the neighbor point configuration unit 63008 may receive theinformation from the signaling processor 61002.

For example, in FIG. 9 , points P2, P4, and P6 may be selected asneighbor points of point (i.e., node) P3 belonging to LOD₁ andregistered in the predictor of P3 as a neighbor set.

According to embodiments, the attribute information prediction unit63009 performs a process of predicting an attribute value of a specificpoint based on the prediction mode of the specific point. According toan embodiment, the attribute prediction is performed on all points or atleast some points of the reconstructed geometry.

The prediction mode of the specific point according to the embodimentsmay be one of prediction modes 0 to 3.

According to embodiments, when the maximum difference between attributevalues of the neighbor points registered in the predictor of the pointis less than a preset threshold, the attribute encoder of thetransmitting side sets prediction mode 0 as the prediction mode of thepoint. When the maximum difference is greater than or equal to thepreset threshold, the attribute encoder applies the RDO method to aplurality of candidate prediction modes, and sets one of the candidateprediction modes as a prediction mode of the point. In an embodiment,this process is performed for each point.

According to embodiments, when the maximum difference between attributevalues of the neighbor points registered in the predictor of the pointis greater than or equal to a preset threshold, the attribute encoder ofthe transmitting side may set a predictor corresponding to predictionmode 0 as a base predictor. Then, the attribute encoder may applyEquation 5 to the base predictor, and Equation 6 to the predictorscorresponding to the neighbor points of the point to calculate scores.Then, the attribute encoder may set a prediction mode corresponding tothe predictor having the lowest score as the prediction mode of thepoint. The prediction mode of the point is set to one of predictionmodes 0 to 3.

According to embodiments, when the maximum difference between attributevalues of the neighbor points registered in the predictor of the pointis greater than or equal to a preset threshold, the attribute encoder ofthe transmitting side may estimate similar attribute values of theneighbor points registered in the point to the attribute value of thepoint, and set a predictor of a neighbor point having the best similarattribute value as a base predictor. Then, Equation 5 may be applied tothe base predictor, and Equation 6 may be applied to the predictorscorresponding to the neighbor points except the neighbor point whosepredictor is set as the base predictor to calculate scores. Then, aprediction mode corresponding to the predictor with the lowest score maybe set as the prediction mode of the point. That is, the predictorcorresponding to prediction mode 0 is not used to set the predictionmode of the point. In other words, the prediction mode of the point isset to one of prediction modes 1 to 3.

According to embodiments, when the maximum difference between attributevalues of the neighbor points registered in the predictor of the pointis greater than or equal to a preset threshold, the attribute encoder ofthe transmitting side may calculate scores by applying Equation 7 to thepredictors corresponding to the neighbor points of the point, and thenset a prediction mode corresponding to the predictor having the lowestscore as the prediction mode of the point. That is, the predictorcorresponding to prediction mode 0 is not used to set the predictionmode of the point. In other words, the prediction mode of the point isset to one of prediction modes 1 to 3.

According to embodiments, a prediction mode (predIndex) of the pointselected by applying the RDO method may be signaled in attribute slicedata. Accordingly, the prediction mode of the point may be obtained fromthe attribute slice data.

According to embodiments, the attribute information prediction unit63009 may predict the attribute value of each point based on theprediction mode of each point that is set as described above.

For example, when it is assumed that the prediction mode of point P3 isprediction mode 0, the average of the values obtained by multiplying theattributes of points P2, P4, and P6, which are neighbor pointsregistered in the predictor of point P3, by a weight (or normalizedweight) may be calculated. The calculated average may be determined asthe predicted attribute value of the point.

As another example, when it is assumed that the prediction mode of pointP3 is prediction mode 1, the attribute value of point P4, which is aneighbor point registered in the predictor of point P3, may bedetermined as the predicted attribute value of point P3.

As another example, when it is assumed that the prediction mode of pointP3 is prediction mode 2, the attribute value of point P6, which is aneighbor point registered in the predictor of point P3, may bedetermined as the predicted attribute value of point P3.

As another example, when it is assumed that the prediction mode of pointP3 is prediction mode 3, the attribute value of point P2, which is aneighbor point registered in the predictor of point P3, may bedetermined as the predicted attribute value of point P3.

Once the attribute information prediction unit 63009 obtains thepredicted attribute value of the point based on the prediction mode ofthe point, the residual attribute information inverse quantizationprocessor 63010 restores the attribute value of the point by adding thepredicted attribute value of the point predicted by the predictor 63009to the received residual attribute value of the point, and then performsinverse quantization as a reverse process to the quantization process ofthe transmission device.

In an embodiment, in the case where the transmitting side applies zerorun-length coding to the residual attribute values of points, theresidual attribute information inverse quantization processor 63010performs zero run-length decoding on the residual attribute values ofthe points, and then performs inverse quantization.

The attribute values restored by the residual attribute informationinverse quantization processor 63010 are output to the inverse colortransformation processor 63011.

The inverse color transformation processor 63011 performs inversetransform coding for inverse transformation of the color values (ortextures) included in the restored attribute values, and then outputsthe attributes to the post-processor 61005. The inverse colortransformation processor 63011 performs an operation and/or inversetransform coding identical or similar to the operation and/or inversetransform coding of the inverse color transformation unit 11010 of FIG.11 or the inverse color transformation processor 13010 of FIG. 13 .

The post-processor 61005 may reconstruct point cloud data by matchingthe positions restored and output by the geometry decoder 61003 with theattributes restored and output by the attribute decoder 61004. Inaddition, when the reconstructed point cloud data is in a tile and/orslice unit, the post-processor 61005 may perform a reverse process tothe spatial partitioning of the transmitting side based on the signalinginformation. For example, when the bounding box shown in FIG. 16(a) ispartitioned into tiles and slices as shown in of FIGS. 16(b) and 16(c),the tiles and/or slices may be combined based on the signalinginformation to restore the bounding box as shown in FIG. 16(a).

FIG. 22 shows an exemplary bitstream structure of point cloud data fortransmission/reception according to embodiments.

When a geometry bitstream, an attribute bitstream, and a signalingbitstream according to embodiments are configured as one bitstream, thebitstream may include one or more sub-bitstreams. The bitstreamaccording to the embodiments may include a sequence parameter set (SPS)for sequence level signaling, a geometry parameter set (GPS) forsignaling of geometry information coding, one or more attributeparameter sets (APSs) (APS₀, APS₁) for signaling of attributeinformation coding, a tile parameter set (TPS) for tile level signaling,and one or more slices (slice 0 to slice n). That is, a bitstream ofpoint cloud data according to embodiments may include one or more tiles,and each of the tiles may be a group of slices including one or moreslices (slice 0 to slice n). The TPS according to the embodiments maycontain information about each of the one or more tiles (e.g.,coordinate value information and height/size information about thebounding box). Each slice may include one geometry bitstream (Geom0) andone or more attribute bitstreams (Attr0 and Attr1). For example, a firstslice (slice 0) may include one geometry bitstream (Geom0⁰) and one ormore attribute bitstreams (Attr0⁰, Attr1⁰).

The geometry bitstream (or geometry slice) in each slice may be composedof a geometry slice header (geom_slice_header) and geometry slice data(geom_slice_data). According to embodiments, geom_slice_header mayinclude identification information (geom_parameter_set_id), a tileidentifier (geom_tile_id), and a slice identifier (geom_slice_id) for aparameter set included in the GPS, and information (geomBoxOrigin,geom_box_log2_scale, geom_max_node_size_log2, geom_num_points) aboutdata contained in the geometry slice data (geom_slice_data).geomBoxOrigin is geometry box origin information indicating the originof the box of the geometry slice data, geom_box_log2_scale isinformation indicating the log scale of the geometry slice data,geom_max_node_size_log2 is information indicating the size of the rootgeometry octree node, and geom_num_points is information related to thenumber of points of the geometry slice data. According to embodiments,the geom_slice_data may include geometry information (or geometry data)about the point cloud data in a corresponding slice.

Each attribute bitstream (or attribute slice) in each slice may becomposed of an attribute slice header (attr_slice_header) and attributeslice data (attr_slice_data). According to embodiments, theattr_slice_header may include information about the correspondingattribute slice data. The attribute slice data may contain attributeinformation (or attribute data or attribute value) about the point clouddata in the corresponding slice. When there is a plurality of attributebitstreams in one slice, each of the bitstreams may contain differentattribute information. For example, one attribute bitstream may containattribute information corresponding to color, and another attributestream may contain attribute information corresponding to reflectance.

FIG. 23 shows an exemplary bitstream structure for point cloud dataaccording to embodiments.

FIG. 24 illustrates a connection relationship between components in abitstream of point cloud data according to embodiments.

The bitstream structure for the point cloud data illustrated in FIGS. 23and 24 may represent the bitstream structure for point cloud data shownin FIG. 22 .

According to the embodiments, the SPS may include an identifier(seq_parameter_set_id) for identifying the SPS, and the GPS may includean identifier (geom_parameter_set_id) for identifying the GPS and anidentifier (seq_parameter_set_id) indicating an active SPS to which theGPS belongs. The APS may include an identifier (attr_parameter_set_id)for identifying the APS and an identifier (seq_parameter_set_id)indicating an active SPS to which the APS belongs.

According to embodiments, a geometry bitstream (or geometry slice) mayinclude a geometry slice header and geometry slice data. The geometryslice header may include an identifier (geom_parameter_set_id) of anactive GPS to be referred to by a corresponding geometry slice.Moreover, the geometry slice header may further include an identifier(geom_slice_id) for identifying a corresponding geometry slice and/or anidentifier (geom_tile_id) for identifying a corresponding tile. Thegeometry slice data may include geometry information belonging to acorresponding slice.

According to embodiments, an attribute bitstream (or attribute slice)may include an attribute slice header and attribute slice data. Theattribute slice header may include an identifier (attr_parameter_set_id)of an active APS to be referred to by a corresponding attribute sliceand an identifier (geom_slice_id) for identifying a geometry slicerelated to the attribute slice. The attribute slice data may includeattribute information belonging to a corresponding slice.

That is, the geometry slice refers to the GPS, and the GPS refers theSPS. In addition, the SPS lists available attributes, assigns anidentifier to each of the attributes, and identifies a decoding method.The attribute slice is mapped to output attributes according to theidentifier. The attribute slice has a dependency on the preceding(decoded) geometry slice and the APS. The APS refers to the SPS.

According to embodiments, parameters necessary for encoding of the pointcloud data may be newly defined in a parameter set of the point clouddata and/or a corresponding slice header. For example, when encoding ofthe attribute information is performed, the parameters may be added tothe APS. When tile-based encoding is performed, the parameters may beadded to the tile and/or slice header.

As shown in FIGS. 22, 23, and 24 , the bitstream of the point cloud dataprovides tiles or slices such that the point cloud data may bepartitioned and processed by regions. According to embodiments, therespective regions of the bitstream may have different importances.Accordingly, when the point cloud data is partitioned into tiles, adifferent filter (encoding method) and a different filter unit may beapplied to each tile. When the point cloud data is partitioned intoslices, a different filter and a different filter unit may be applied toeach slice.

When the point cloud data is partitioned and compressed, thetransmission device and the reception device according to theembodiments may transmit and receive a bitstream in a high-level syntaxstructure for selective transmission of attribute information in thepartitioned regions.

The transmission device according to the embodiments may transmit pointcloud data according to the bitstream structure as shown in FIGS. 22,23, and 24 . Accordingly, a method to apply different encodingoperations and use a good-quality encoding method for an importantregion may be provided. In addition, efficient encoding and transmissionmay be supported according to the characteristics of point cloud data,and attribute values may be provided according to user requirements.

The reception device according to the embodiments may receive the pointcloud data according to the bitstream structure as shown in FIGS. 22,23, and 24 . Accordingly, different filtering (decoding) methods may beapplied to the respective regions (regions partitioned into tiles orinto slices), rather than a complexly decoding (filtering) method beingapplied to the entire point cloud data. Therefore, better image qualityin a region important is provided to the user and an appropriate latencyto the system may be ensured.

As described above, a tile or a slice is provided to process the pointcloud data by partitioning the point cloud data by region. Inpartitioning the point cloud data by region, an option to generate adifferent set of neighbor points for each region may be set. Thereby, aselection method having low complexity and slightly lower reliability,or a selection method having high complexity and high reliability I maybe provided.

At least one of the SPS, APS, TPS, or attribute slice headers forrespective slices according to embodiments may include optioninformation related to predictor selection. According to embodiments,the predictor selection related option information may include a basepredictor selection method (attribute_base_predictor selection_type) andan attribute similarity check method(attribute_similarity_check_method_type).

A field, which is a term used in syntaxes of the present disclosuredescribed below, may have the same meaning as a parameter or element.

FIG. 25 shows an embodiment of a syntax structure of a sequenceparameter set (SPS) (seq_parameter_set_rbsp( ) according to the presentdisclosure. The SPS may include sequence information about a point clouddata bitstream. In particular, in this example, the SPS includespredictor selection related option information.

The SPS according to the embodiments may include a profile_idc field, aprofile_compatibility_flags field, a level_idc field, ansps_bounding_box_present_flag field, an sps_source_scale_factor field,an sps_seq_parameter_set_id field, an sps_num_attribute_sets field, andan sps_extension_present_flag field.

The profile_idc field indicates a profile to which the bitstreamconforms.

The profile_compatibility_flags field equal to 1 may indicate that thebitstream conforms to the profile indicated by profile_idc.

The level_idc field indicates a level to which the bitstream conforms.

The sps_bounding_box_present_flag field indicates whether sourcebounding box information is signaled in the SPS. The source bounding boxinformation may include offset and size information about the sourcebounding box. For example, the sps_bounding_box_present_flag field equalto 1 indicates that the source bounding box information is signaled inthe SPS. The sps_bounding_box_present_flag field equal to 0 indicatesthe source bounding box information is not signaled. Thesps_source_scale_factor field indicates the scale factor of the sourcepoint cloud.

The sps_seq_parameter_set_id field provides an identifier for the SPSfor reference by other syntax elements.

The sps_num_attribute_sets field indicates the number of codedattributes in the bitstream.

The sps_extension_present_flag field specifies whether thesps_extension_data syntax structure is present in the SPS syntaxstructure. For example, the sps_extension_present_flag field equal to 1specifies that the sps_extension_data syntax structure is present in theSPS syntax structure. The sps_extension_present_flag field equal to 0specifies that this syntax structure is not present. When not present,the value of the sps_extension_present_flag field is inferred to beequal to 0.

When the sps_bounding_box_present_flag field is equal to 1, the SPSaccording to embodiments may further include ansps_bounding_box_offset_x field, an sps_bounding_box_offset_y field, ansps_bounding_box_offset_z field, an sps_bounding_box_scale_factor field,an sps_bounding_box_size_width field, an sps_bounding_box_size_heightfield, and an sps_bounding_box_size_depth field.

The sps_bounding_box_offset_x field indicates the x offset of the sourcebounding box in the Cartesian coordinates. When the x offset of thesource bounding box is not present, the value ofsps_bounding_box_offset_x is 0.

The sps_bounding_box_offset_y field indicates the y offset of the sourcebounding box in the Cartesian coordinates. When the y offset of thesource bounding box is not present, the value ofsps_bounding_box_offset_y is 0.

The sps_bounding_box_offset_z field indicates the z offset of the sourcebounding box in the Cartesian coordinates. When the z offset of thesource bounding box is not present, the value ofsps_bounding_box_offset_z is 0.

The sps_bounding_box_scale_factor field indicates the scale factor ofthe source bounding box in the Cartesian coordinates. When the scalefactor of the source bounding box is not present, the value ofsps_bounding_box_scale_factor may be 1.

The sps_bounding_box_size_width field indicates the width of the sourcebounding box in the Cartesian coordinates. When the width of the sourcebounding box is not present, the value of thesps_bounding_box_size_width field may be 1.

The sps_bounding_box_size_height field indicates the height of thesource bounding box in the Cartesian coordinates. When the height of thesource bounding box is not present, the value of thesps_bounding_box_size_height field may be 1.

The sps_bounding_box_size_depth field indicates the depth of the sourcebounding box in the Cartesian coordinates. When the depth of the sourcebounding box is not present, the value of thesps_bounding_box_size_depth field may be 1.

The SPS according to embodiments includes an iteration statementrepeated as many times as the value of the sps_num_attribute_sets field.In an embodiment, i is initialized to 0, and is incremented by 1 eachtime the iteration statement is executed. The iteration statement isrepeated until the value of i becomes equal to the value of thesps_num_attribute_sets field. The iteration statement may include anattribute_dimension[i] field, an attribute_instance_id[i] field, anattribute_bitdepth[i] field, an attribute_cicp_colour_primaries[i]field, an attribute_cicp_transfer_characteristics[i] field, anattribute_cicp_matrix_coeffs[i] field, anattribute_cicp_video_full_range_flag[i] field, and aknown_attribute_label_flag[i] field.

The attribute_dimension[i] field specifies the number of components ofthe i-th attribute.

The attribute_instance_id[i] field specifies the instance ID of the i-thattribute.

The attribute_bitdepth[i] field specifies the bitdepth of the i-thattribute signal(s).

The attribute_cicp_colour_primaries[i] field indicates chromaticitycoordinates of the color attribute source primaries of the i-thattribute.

The attribute_cicp_transfer_characteristics[i] field either indicatesthe reference opto-electronic transfer characteristic function of thecolour attribute as a function of a source input linear opticalintensity with a nominal real-valued range of 0 to 1 or indicates theinverse of the reference electro-optical transfer characteristicfunction as a function of an output linear optical intensity.

The attribute_cicp_matrix_coeffs[i] field describes the matrixcoefficients used in deriving luma and chroma signals from the green,blue, and red, or Y, Z, and X primaries.

The attribute_cicp_video_full_range_flag[i] field indicates the blacklevel and range of the luma and chroma signals as derived from E′Y,E′PB, and E′PR or E′R, E′G, and E′B real-valued component signals.

The known_attribute_label_flag[i] field specifies whether aknown_attribute_label field or an attribute_label_four_bytes field issignaled for the i-th attribute. For example, the value of theknown_attribute_label_flag[i] field equal to 0 specifies that theknown_attribute_label field is signaled for the ith attribute. Theknown_attribute_label_flag[i] field equal to 1 specifies that theattribute_label_four_bytes field is signaled for the ith attribute.

The known_attribute_label[i] field may specify an attribute type. Forexample, the known_attribute_label[i] field equal to 0 may specify thatthe i-th attribute is color. The known_attribute_label[i] field equal to1 specifies that the i-th attribute is reflectance. Theknown_attribute_label[i] field equal to 2 may specify that the i-thattribute is frame index.

The attribute_label_four_bytes field indicates the known attribute typewith a 4-byte code.

In this example, the attribute_label_four_bytes field indicates colorwhen equal to 0 and indicates reflectance when is equal to 1.

According to embodiments, when the sps_extension_present_flag field isequal to 1, the SPS may further include a sps_extension_data flag field.

The sps_extension_data flag field may have any value.

According to embodiments, the SPS may further include predictorselection related option information. According to embodiments, thepredictor selection related option information may include a basepredictor selection method (attribute_base_predictor selection_type) anda attribute similarity check method(attribute_similarity_check_method_type).

According to embodiments, the predictor selection related optioninformation may be included in an iteration statement repeated as manytimes as the value of the sps_num_attribute_sets field described above.

That is, the iteration statement may include an attribute_base_predictorselection_type [i] field and an attribute_similarity_check_method_type[i] field.

The attribute_base_predictor selection_type [i] field may specify amethod of selecting a base predictor of the i-th attribute. For example,the attribute_base_predictor selection_type [i] field equal to 0indicates that the base predictor is selected based on the weightedaverage of neighbors, and the attribute_base_predictor selection_type[i] field equal to 1 indicates that the base predictor is selected basedon attribute similarity.

The attribute_similarity_check_method_type [i] field may indicate anattribute similarity check method when the attribute similarity of thei-th attribute is referenced. For example, theattribute_similarity_check_method_type [i] field equal to 0 indicatesthat the Euclidian color distance such as Equation 8 is used. Theattribute_similarity_check_method_type [i] field equal to 1 indicatesthat the correlated color temperature such as Equation 9 is used. Theattribute_similarity_check_method_type [i] field equal to 2 indicatesthat CIE94 such as Equation 10 is used.

In this way, the attribute_base_predictor selection_type [i] field andthe attribute_similarity_check_method_type [i] field may be signaled inthe SPS.

FIG. 26 shows an embodiment of a syntax structure of the geometryparameter set (GPS) (geometry_parameter_set( )) according to the presentdisclosure. The GPS according to the embodiments may contain informationon a method of encoding geometry information about point cloud datacontained in one or more slices.

According to embodiments, the GPS may include agps_geom_parameter_set_id field, a gps_seq_parameter_set_id field, agps_box_present_flag field, a unique_geometry_points_flag field, aneighbour context restriction flag field, aninferred_direct_coding_mode_enabled_flag field, abitwise_occupancy_coding_flag field, anadjacent_child_contextualization_enabled_flag field, alog2_neighbour_avail_boundary field, a log2_intra_pred_max_node_sizefield, a log2_trisoup_node_size field, and a gps_extension_present_flagfield.

The gps_geom_parameter_set_id field provides an identifier for the GPSfor reference by other syntax elements.

The gps_seq_parameter_set_id field specifies the value ofsps_seq_parameter_set_id for the active SPS.

The gps_box_present_flag field specifies whether additional bounding boxinformation is provided in a geometry slice header that references thecurrent GPS. For example, the gps_box_present_flag field equal to 1 mayspecify that additional bounding box information is provided in ageometry header that references the current GPS. Accordingly, when thegps_box_present_flag field is equal to 1, the GPS may further include agps_gsh_box_log2_scale_present_flag field.

The gps_gsh_box_log2_scale_present_flag field specifies whether thegps_gsh_box_log2_scale field is signaled in each geometry slice headerthat references the current GPS. For example, thegps_gsh_box_log2_scale_present_flag field equal to 1 may specify thatthe gps_gsh_box_log2_scale field is signaled in each geometry sliceheader that references the current GPS. As another example, thegps_gsh_box_log2_scale_present_flag field equal to 0 may specify thatthe gps_gsh_box_log2_scale field is not signaled in each geometry sliceheader and a common scale for all slices is signaled in thegps_gsh_box_log2_scale field of the current GPS.

When the gps_gsh_box_log2_scale_present_flag field is equal to 0, theGPS may further include a gps_gsh_box_log2_scale field.

The gps_gsh_box_log2_scale field indicates the common scale factor ofthe bounding box origin for all slices that refer to the current GPS.

The unique_geometry_points_flag field indicates whether all outputpoints have unique positions. For example, theunique_geometry_points_flag field equal to 1 indicates that all outputpoints have unique positions. The unique_geometry_points_flag fieldequal to 0 indicates that in all slices that refer to the current GPS,the two or more of the output points may have the same position.

The neighbor_context_restriction_flag field indicates contexts used foroctree occupancy coding. For example, theneighbour_context_restriction_flag field equal to 0 indicates thatoctree occupancy coding uses contexts determined from six neighboringparent nodes. The neighbour_context_restriction_flag field equal to 1indicates that octree occupancy coding uses contexts determined fromsibling nodes only.

The inferred_direct_coding_mode_enabled_flag field indicates whether thedirect_mode_flag field is present in the geometry node syntax. Forexample, the inferred_direct_coding_mode_enabled_flag field equal to 1indicates that the direct_mode_flag field may be present in the geometrynode syntax. For example, the inferred_direct_coding_mode_enabled_flagfield equal to 0 indicates that the direct_mode_flag field is notpresent in the geometry node syntax.

The bitwise_occupancy_coding_flag field indicates whether geometry nodeoccupancy is encoded using bitwise contextualization of the syntaxelement occupancy map. For example, the bitwise_occupancy_coding_flagfield equal to 1 indicates that geometry node occupancy is encoded usingbitwise contextualisation of the syntax element occupancy_map. Forexample, the bitwise_occupancy_coding_flag field equal to 0 indicatesthat geometry node occupancy is encoded using the dictionary encodedsyntax element occupancy byte.

The adjacent_child_contextualization_enabled_flag field indicateswhether the adjacent children of neighboring octree nodes are used forbitwise occupancy contextualization. For example, theadjacent_child_contextualization_enabled_flag field equal to 1 indicatesthat the adjacent children of neighboring octree nodes are used forbitwise occupancy contextualization. For example,adjacent_child_contextualization_enabled_flag equal to 0 indicates thatthe children of neighbouring octree nodes are not used for the occupancycontextualization.

The log2_neighbour_avail_boundary field specifies the value of thevariable NeighbAvailBoundary that is used in the decoding process asfollows:NeighbAvailBoundary=2^(log2_neighbour_avail_boundary)

For example, when the neighbour_context_restriction_flag field is equalto 1, NeighbAvailabilityMask may be set equal to 1. For example, whenthe neighbour_context_restriction_flag field is equal to 0,NeighbAvailabilityMask may be set equal to 1<<log2neighbour_avail_boundary.

The log2_intra_pred_max_node_size field specifies the octree node sizeeligible for occupancy intra prediction.

The log2_trisoup_node_size field specifies the variable TrisoupNodeSizeas the size of the triangle nodes as follows.TrisoupNodeSize=1<<log2_trisoup_node_size

The gps_extension_present_flag field specifies whether thegps_extension_data syntax structure is present in the GPS syntaxstructure. For example, gps_extension_present_flag equal to 1 specifiesthat the gps_extension_data syntax structure is present in the GPSsyntax. For example, gps_extension_present_flag equal to 0 specifiesthat this syntax structure is not present in the GPS syntax.

When the value of the gps_extension_present_flag field is equal to 1,the GPS according to the embodiments may further include agps_extension_data_flag field.

The gps_extension_data_flag field may have any value. Its presence andvalue do not affect the decoder conformance to profiles.

FIG. 27 shows an embodiment of a syntax structure of the attributeparameter set (APS) (attribute_parameter_set( )) according to thepresent disclosure. The APS according to the embodiments may containinformation on a method of encoding attribute information in point clouddata contained in one or more slices. According to embodiments, the APSmay include predictor selection related option information.

The APS according to the embodiments may include anaps_attr_parameter_set_id field, an aps_seq_parameter_set_id field, anattr_coding_type field, an aps_attr_initial_qp field, anaps_attr_chroma_qp_offset field, an aps_slice_qp_delta_present_flagfield, and an aps_flag field.

The aps_attr_parameter_set_id field provides an identifier for the APSfor reference by other syntax elements.

The aps_seq_parameter_set_id field specifies the value ofsps_seq_parameter_set_id for the active SPS.

The attr_coding_type field indicates the coding type for the attribute.

In this example, the attr_coding_type field equal to 0 indicatespredicting weight lifting as the coding type. The attr_coding_type fieldequal to 1 indicates RAHT as the coding type. The attr_coding_type fieldequal to 2 indicates fix weight lifting.

The aps_attr_initial_qp field specifies the initial value of thevariable SliceQp for each slice referring to the APS. The initial valueof SliceQp is modified at the attribute slice segment layer when anon-zero value of slice_qp_delta_luma or slice_qp_delta_luma are decoded

The aps_attr_chroma_qp_offset field specifies the offsets to the initialquantization parameter signaled by the syntax aps_attr_initial_qp.

The aps_slice_qp_delta_present_flag field specifies whether theash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements arepresent in the attribute slice header (ASH). For example, theaps_slice_qp_delta_present_flag field equal to 1 specifies that theash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements arepresent in the ASH. For example, the aps_slice_qp_delta_present_flagfield equal to 0 specifies that the ash_attr_qp_delta_luma andash_attr_qp_delta_chroma syntax elements are not present in the ASH.

When the value of the attr_coding_type field is 0 or 2, that is, thecoding type is predicting weight lifting or fix weight lifting, the APSaccording to the embodiments may further include alifting_num_pred_nearest_neighbours field, alifting_max_num_direct_predictors

, a lifting_search_range field, alifting_lod_regular_sampling_enabled_flag field, alifting_num_detail_levels_minus1 field.

The lifting_num_pred_nearest_neighbours field specifies the maximumnumber of nearest neighbors to be used for prediction.

The lifting_max_num_direct_predictors field specifies the maximum numberof predictors to be used for direct prediction. The value of thevariable MaxNumPredictors that is used in the decoding process asfollows:MaxNumPredictors=lifting_max_num_direct_predictors field+1

The lifting_lifting_search_range field specifies the search range usedto determine nearest neighbors to be used for prediction and to builddistance-based levels of detail.

The lifting_num_detail_levels_minus1 field specifies the number oflevels of detail for the attribute coding.

The lifting_lod_regular_sampling_enabled_flag field specifies whetherlevels of detail (LOD) are built by a regular sampling strategy. Forexample, the lifting_lod_regular_sampling_enabled_flag equal to 1specifies that levels of detail (LOD) are built by using a regularsampling strategy. The lifting_lod_regular_sampling_enabled_flag equalto 0 specifies that a distance-based sampling strategy is used instead.

The APS according to embodiments includes an iteration statementrepeated as many times as the value of thelifting_num_detail_levels_minus1 field. In an embodiment, the index(idx) is initialized to 0 and incremented by 1 every time the iterationstatement is executed, and the iteration statement is repeated until theindex (idx) is greater than the value of thelifting_num_detail_levels_minus1 field. This iteration statement mayinclude a lifting_sampling_period[idx] field when the value of thelifting_lod_decimation_enabled_flag field is true (e.g., 1), and mayinclude a lifting_sampling_distance_squared[idx] field when the value ofthe lifting_lod_decimation_enabled_flag field is false (e.g., 0).

The lifting_sampling_period[idx] field specifies the sampling period forthe level of detail idx.

The lifting_sampling_distance_squared[idx] field specifies the square ofthe sampling distance for the level of detail idx.

When the value of the attr_coding_type field is 0, that is, when thecoding type is predicting weight lifting, the APS according to theembodiments may further include a lifting_adaptive_prediction_thresholdfield, and a lifting_intra_lod_prediction_num_layers field.

The lifting_adaptive_prediction_threshold field specifies the thresholdto enable adaptive prediction.

The lifting_intra_lod_prediction_num_layers field specifies the numberof LOD layers where decoded points in the same LOD layer could bereferred to generate a prediction value of a target point. For example,the lifting_intra_lod_prediction_num_layers field equal tonum_detail_levels_minus1 plus 1 indicates that target point could referto decoded points in the same LOD layer for all LOD layers. For example,the lifting_intra_lod_prediction_num_layers field equal to 0 indicatesthat target point could not refer to decoded points in the same LoDlayer for any LoD layers.

The aps_extension_present_flag field specifies whether theaps_extension_data syntax structure is present in the APS syntaxstructure. For example, the aps_extension_present_flag field equal to 1specifies that the aps_extension_data syntax structure is present in theAPS syntax structure. For example, the aps_extension_present_flag fieldequal to 0 specifies that this syntax structure is not present in theAPS syntax structure.

When the value of the aps_extension_present_flag field is 1, the APSaccording to the embodiments may further include an aps_extension_dataflag field.

The aps_extension_data flag field may have any value. Its presence andvalue do not affect decoder conformance to profiles.

The APS according to the embodiments may further include predictorselection related option information.

When the value of the attr_coding_type field is 0 or 2, that is, whenthe coding type is “predicting weight lifting” or “fix weight lifting,”the APS according to embodiments may further include anattribute_base_predictor selection_type field and anattribute_similarity_check_method_type field.

The attribute_base_predictor selection_type field may specify a methodof selecting a base predictor. For example, the attribute_base_predictorselection_type field equal to 0 indicates that the base predictor isselected based on the weighted average of neighbors, and theattribute_base_predictor selection_type field equal to 1 indicates thatthe base predictor is selected based on attribute similarity.

The attribute_similarity_check_method_type field may indicate anattribute similarity check method. For example, theattribute_similarity_check_method_type field equal to 0 indicates thatthe Euclidian color distance such as Equation 8 is used. Theattribute_similarity_check_method_type field equal to 1 indicates thatthe correlated color temperature such as Equation 9 is used. Theattribute_similarity_check_method_type field equal to 2 indicates thatCIE94 such as Equation 10 is used.

In this way, the attribute_base_predictor selection_type field and theattribute_similarity_check_method_type field may be signaled in the APS.

FIG. 28 shows an embodiment of a syntax structure of a tile parameterset (TPS) (tile_parameter_set( )) according to the present disclosure.According to embodiments, a TPS may be referred to as a tile inventory.The TPS according to the embodiments includes information related toeach tile. In particular, in this example, the TPS includes predictorselection related option information.

The TPS according to the embodiments includes a num_tiles field.

The num_tiles field indicates the number of tiles signaled for thebitstream. When not present, num_tiles is inferred to be 0.

The TPS according to the embodiments includes an iteration statementrepeated as many times as the value of the num_tiles field. In anembodiment, i is initialized to 0, and is incremented by 1 each time theiteration statement is executed. The iteration statement is repeateduntil the value of i becomes equal to the value of the num_tiles field.The iteration statement may include a tile_bounding_box_offset_x[i]field, a tile_bounding_box_offset_y[i] field, atile_bounding_box_offset_z[i] field, a tile_bounding_box_size_width[i]field, a tile_bounding_box_size_height[i] field, and atile_bounding_box_size_depth[i] field.

The tile_bounding_box_offset_x[i] field indicates the x offset of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_offset_y[i] field indicates the y offset of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_offset_z[i] field indicates the z offset of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_size_width[i] field indicates the width of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_size_height[i] field indicates the height of thei-th tile in the Cartesian coordinates.

The tile_bounding_box_size_depth[i] field indicates the depth of thei-th tile in the Cartesian coordinates.

The TPS according to the embodiments may further include predictorselection related option information.

According to embodiments, the predictor selection related optioninformation may be included in an iteration statement repeated as manytimes as the value of the num_tiles field described above.

That is, the iteration statement may include an attribute_base_predictorselection_type [i] field and an attribute_similarity_check_method_type[i] field.

The attribute_base_predictor selection_type [i] field may specify amethod of selecting a base predictor in the i-th tile. For example, theattribute_base_predictor selection_type [i] field equal to 0 indicatesthat the base predictor is selected based on the weighted average ofneighbors, and the attribute_base_predictor selection_type [i] fieldequal to 1 indicates that the base predictor is selected based onattribute similarity.

The attribute_similarity_check_method_type [i] field may indicate anattribute similarity check method in the i-th tile. For example, theattribute_similarity_check_method_type [i] field equal to 0 indicatesthat the Euclidian color distance such as Equation 8 is used. Theattribute_similarity_check_method_type [i] field equal to 1 indicatesthat the correlated color temperature such as Equation 9 is used. Theattribute_similarity_check_method_type [i] field equal to 2 indicatesthat CIE94 such as Equation 10 is used.

In this way, the attribute_base_predictor selection_type [i] field andthe attribute_similarity_check_method_type [i] field may be signaled inthe TPS.

FIG. 29 shows an embodiment of a syntax structure of a geometry slicebitstream( ) according to the present disclosure.

The geometry slice bitstream (geometry_slice_bitstream( )) according tothe embodiments may include a geometry slice header(geometry_slice_header( )) and geometry slice data (geometry_slice_data()).

FIG. 30 shows an embodiment of a syntax structure of thegeometry_slice_header (geometry_slice_header( )) according to thepresent disclosure.

A bitstream transmitted by the transmission device (or a bitstreamreceived by the reception device) according to the embodiments maycontain one or more slices. Each slice may include a geometry slice andan attribute slice. The geometry slice includes a geometry slice header(GSH). The attribute slice includes an attribute slice header (ASH).

The geometry slice header (geometry_slice_header( )) according toembodiments may include a gsh_geom_parameter_set_id field, a gsh_tile_idfield, a gsh_slice_id field, a gsh_max_node_size_log2 field, agsh_num_points field, and a byte_alignment( ) field.

When the value of the gps_box_present_flag field included in the GPS is‘true’ (e.g., 1), and the value of thegps_gsh_box_log2_scale_present_flag field is ‘true’ (e.g., 1), thegeometry slice header (geometry_slice_header( )) according to theembodiments may further include a gsh_box_log2_scale field, agsh_box_origin_x field, a gsh_box_origin_y field, and a gsh_box_origin_zfield.

The gsh_geom_parameter_set_id field specifies the value of thegps_geom_parameter_set_id of the active GPS.

The gsh_tile_id field specifies the value of the tile id that isreferred to by the GSH.

The gsh_slice_id specifies id of the slice for reference by other syntaxelements.

The gsh_box_log2_scale field specifies the scaling factor of thebounding box origin for the slice.

The gsh_box_origin_x field specifies the x value of the bounding boxorigin scaled by the value of the gsh_box_log2_scale field.

The gsh_box_origin_y field specifies they value of the bounding boxorigin scaled by the value of the gsh_box_log2_scale field.

The gsh_box_origin_z field specifies the z value of the bounding boxorigin scaled by the value of the gsh_box_log2_scale field.

The gsh_max_node_size_log2 field specifies a size of a root geometryoctree node.

The gsh_points_number field specifies the number of coded points in theslice.

FIG. 31 shows an embodiment of a syntax structure of geometry slice data(geometry_slice_data( )) according to the present disclosure. Thegeometry slice data (geometry_slice_data( )) according to theembodiments may carry a geometry bitstream belonging to a correspondingslice.

The geometry_slice_data( ) according to the embodiments may include afirst iteration statement repeated as many times as by the value ofMaxGeometryOctreeDepth. In an embodiment, the depth is initialized to 0and is incremented by 1 each time the iteration statement is executed,and the first iteration statement is repeated until the depth becomesequal to MaxGeometryOctreeDepth. The first iteration statement mayinclude a second loop statement repeated as many times as the value ofNumNodesAtDepth. In an embodiment, nodeidx is initialized to 0 and isincremented by 1 each time the iteration statement is executed. Thesecond iteration statement is repeated until nodeidx becomes equal toNumNodesAtDepth. The second iteration statement may includexN=NodeX[depth][nodeIdx], yN=NodeY[depth][nodeIdx],zN=NodeZ[depth][nodeIdx], and geometry_node(depth, nodeIdx, xN, yN, zN).MaxGeometryOctreeDepth indicates the maximum value of the geometryoctree depth, and NumNodesAtDepth[depth] indicates the number of nodesto be decoded at the corresponding depth. The variables NodeX[depth][nodeIdx], NodeY[depth] [nodeIdx], and NodeZ[depth] [nodeIdx] indicatethe x, y, z coordinates of the nodeIdx-th node in decoding order at agiven depth. The geometry bitstream of the node of the depth istransmitted through geometry node(depth, nodeIdx, xN, yN, zN).

The geometry slice data (geometry_slice_data( )) according to theembodiments may further include geometry trisoup data( ) when the valueof the log2_trisoup_node_size field is greater than 0. That is, when thesize of the triangle nodes is greater than 0, a geometry bitstreamsubjected to trisoup geometry encoding is transmitted throughgeometry_trisoup_data( ).

FIG. 32 shows an embodiment of a syntax structure ofattribute_slice_bitstream( ) according to the present disclosure.

The attribute slice bitstream (attribute_slice_bitstream( )) accordingto the embodiments may include an attribute slice header(attribute_slice_header( )) and attribute slice data (attribute slicedata( )).

FIG. 33 shows an embodiment of a syntax structure of an attribute sliceheader (attribute_slice_header( )) according to the present disclosure.The attribute slice header according to the embodiments includessignaling information for a corresponding attribute slice. Inparticular, in this example, the attribute slice header includespredictor selection related option information.

The attribute slice header (attribute_slice_header( )) according to theembodiments may include an ash_attr_parameter_set_id field, anash_attr_sps_attr_idx field, and an ash_attr_geom_slice_id field.

When the value of the aps_slice_qp_delta_present_flag field of the APSis ‘true’ (e.g., 1), the attribute slice header (attribute_slice_header()) according to the embodiments may further include an ash_qp_delta_lumafield and an ash_qp_delta_chroma field.

The ash attr_parameter_set_id field specifies a value of theaps_attr_parameter_set_id field of the current active APS (e.g., theaps_attr_parameter_set_id field included in the APS described in FIG. 27).

The ash_attr_sps_attr_idx field identifies an attribute set in thecurrent active SPS. The value of the ash_attr_sps_attr_idx field is inthe range from 0 to the sps_num_attribute_sets field included in thecurrent active SPS.

The ash_attr_geom_slice_id field specifies the value of the gsh_slice_idfield of the current geometry_slice_header.

The ash_qp_delta_luma field specifies a luma delta quantizationparameter (qp) derived from the initial slice qp in the active attributeparameter set.

The ash_qp_delta_chroma field specifies the chroma delta qp derived fromthe initial slice qp in the active attribute parameter set.

The attribute slice header (attribute_slice_header( )) according to theembodiments may further include predictor selection related optioninformation, for example, an attribute_base_predictor selection_typefield and an attribute_similarity_check_method_type field.

The attribute_base_predictor selection_type field may specify a methodof selecting a base predictor. For example, the attribute_base_predictorselection_type field equal to 0 indicates that the base predictor isselected based on the weighted average of neighbors, and theattribute_base_predictor selection_type field equal to 1 indicates thatthe base predictor is selected based on attribute similarity.

The attribute_similarity_check_method_type field may indicate anattribute similarity check method. For example, theattribute_similarity_check_method_type field equal to 0 indicates thatthe Euclidian color distance such as Equation 8 is used. Theattribute_similarity_check_method_type field equal to 1 indicates thatthe correlated color temperature such as Equation 9 is used. Theattribute_similarity_check_method_type field equal to 2 indicates thatCIE94 such as Equation 10 is used.

In this way, the attribute_base_predictor selection_type field and theattribute_similarity_check_method_type field may be signaled in theattribute slice header (attribute_slice_header( )).

FIG. 34 shows an embodiment of a syntax structure of the attribute slicedata (attribute_slice_data( )) according to the present disclosure. Theattribute slice data (attribute_slice_data( )) according to theembodiments may carry an attribute bitstream belonging to acorresponding slice.

In the attribute slice data (attribute_slice_data( )) of FIG. 34 ,dimension=attribute_dimension[ash_attr_sps_attr_idx] represents theattribute dimension (attribute_dimension) of the attribute setidentified by the ash_attr_sps_attr_idx field in the correspondingattribute slice header. The attribute_dimension refers to the number ofcomponents constituting an attribute. An attribute according toembodiments represent reflectance, color, or the like. Accordingly, thenumber of components varies among attributes. For example, an attributecorresponding to color may have three color components (e.g., RGB).Accordingly, an attribute corresponding to reflectance may be amono-dimensional attribute, and the attribute corresponding to color maybe a three-dimensional attribute.

The attributes according to the embodiments may be attribute-encoded ona dimension-by-dimension basis.

For example, the attribute corresponding to reflectance and theattribute corresponding to color may be attribute-encoded, respectively.According to embodiments, attributes may be attribute-encoded togetherregardless of dimensions. For example, the attribute corresponding toreflectance and the attribute corresponding to color may beattribute-encoded together.

In FIG. 34 , zerorun specifies the number of 0 prior to residual).

In FIG. 34 , i denotes an i-th point value of the attribute. Accordingto an embodiment, the attr_coding_type field and thelifting_adaptive_prediction_threshold field are signaled in the APS.

MaxNumPredictors of FIG. 34 is a variable used in the point cloud datadecoding process, and may be acquired based on the value of thelifting_adaptive_prediction_threshold field signaled in the APS asfollows.MaxNumPredictors=lifting_max_num_direct_predictors field+1

Here, the lifting_max_num_direct_predictors field indicates the maximumnumber of predictors to be used for direct prediction.

According to the embodiments, predIndex[i] specifies the predictor index(or prediction mode) to decode the i-th point value of the attribute.The value of predIndex[i] is in the range from 0 to the value of thelifting_max_num_direct_predictors field.

The variable MaxPredDiff[i] according to the embodiments may becalculated as follows.minValue=maxValue=ã ₀

for (j=0; j<k; j++) {

-   -   minValue=Min(minValue, ã_(j))    -   maxValue=Max(maxValue, ã_(j))

}

MaxPredDiff[i]=maxValue−minValue;

Here, let k_(i) be the set of the k-nearest neighbors of the currentpoint i and let

be their decoded/reconstructed attribute values. The number of nearestneighbors, k_(i) shall be in the range of 1 tolifting_num_pred_nearest_neighbours. According to embodiments, thedecoded/reconstructed attribute values of neighbors are derivedaccording to the Predictive Lifting decoding process.

The lifting_num_pred_nearest_neighbours field is signaled in the APS andindicates the maximum number of nearest neighbors to be used forprediction.

FIG. 35 is a flowchart of a method of transmitting point cloud dataaccording to embodiments.

The point cloud data transmission method according to the embodimentsmay include a step 71001 of encoding geometry contained in the pointcloud data, a step 71002 of encoding an attribute contained in the pointcloud data based on input and/or reconstructed geometry, and a step71003 of transmitting a bitstream including the encoded geometry, theencoded attribute, and signaling information.

The steps 71001 and 71002 of encoding the geometry and attributecontained in the point cloud data may perform some or all of theoperations of the point cloud video encoder 10002 of FIG. 1 , theencoding process 20001 of FIG. 2 , the point cloud video encoder of FIG.4 , the point cloud video encoder of FIG. 12 , the point cloud encodingprocess of FIG. 14 , the point cloud video encoder of FIG. 15 or thegeometry encoder and the attribute encoder of FIG. 17 .

According to embodiments, a prediction mode of a specific point that isset in the step 71002 may be one of a prediction mode 0, a predictionmode 1, a prediction mode 2, or a prediction mode 3.

According to embodiments, when a maximum difference value betweenattribute values of nearest neighbor points that are registered in apredictor of a corresponding point to be encoded is less than apre-defined threshold value, the step 71002 sets the prediction mode 0to a prediction mode of the point. According to embodiments, when themaximum difference value between the attribute values of the nearestneighbor points that are registered in the predictor of the point to beencoded is equal or greater than the pre-defined threshold value, thestep 71002 applies a rate-distortion optimization (RDO) procedure to aplurality of candidate prediction modes and sets one of the plurality ofcandidate prediction modes to the prediction mode of the point. In anembodiment, this process is performed for each point.

According to an embodiment, when the maximum difference value betweenthe attribute values of the nearest neighbor points that are registeredin the predictor of the point to be encoded is equal or greater than thepre-defined threshold value, the step 71002 sets a predictorcorresponding to the prediction mode 0 to a base predictor andcalculates scores (double scores) by applying Equation 5 to the basepredictor and applying Equation 6 to predictors corresponding toneighbor points of the point to be encoded. Then, the step 71002 sets aprediction mode corresponding a predictor having a smallest score of thecalculated scores to a prediction mode of the point. According to anembodiment, the prediction mode that is set to the point may be one ofthe prediction mode 0 to the prediction mode 3.

According to another embodiment, when the maximum difference valuebetween the attribute values of the nearest neighbor points that areregistered in the predictor of the point to be encoded is equal orgreater than the pre-defined threshold value, the step 71002 estimatessimilar attribute values between the attribute value of the point andattribute values of the neighbor points registered in the point, andsets a predictor corresponding to a neighbor point having a best similarattribute value of the similar attribute values to a base predictor, andthen calculates scores (double scores) by applying Equation 5 to thebase predictor and applying Equation 6 to the remaining predictors(excluding the predictor set as the base predictor) corresponding toneighbor points of the point to be encoded. Then, the step 71002 sets aprediction mode corresponding a predictor having a smallest score of thecalculated scores to a prediction mode of the point. That is, thepredictor corresponding to the prediction mode 0 is not used to set aprediction mode of the point to be encoded. In other words, theprediction mode that is set to the point may be one of the predictionmode 1 to the prediction mode 3.

According to another embodiment, when the maximum difference valuebetween the attribute values of the nearest neighbor points that areregistered in the predictor of the point to be encoded is equal orgreater than the pre-defined threshold value, the step 71002 calculatesscores (double scores) by applying Equation 7 to predictorscorresponding to neighbor points of the point to be encoded and sets aprediction mode corresponding a predictor having a smallest score of thecalculated scores to a prediction mode of the point. That is, thepredictor corresponding to the prediction mode 0 is not used to set aprediction mode of the point to be encoded. In other words, theprediction mode that is set to the point may be one of the predictionmode 1 to the prediction mode 3.

According to embodiments, the prediction mode of the point selected byapplying a RDO procedure may be signaled in an attribute slice data.

According to embodiments, the step 71002 may apply a zero-run lengthcoding to the residual attribute values of the points.

In the steps 71001 and 71002 according to embodiments, encoding may beperformed on the basis of a slice or a tile containing one or moreslices.

The step 71003 may be performed by the transmitter 10003 of FIG. 1 , thetransmitting process 20002 of FIG. 2 , transmission processor 12012 ofFIG. 12 or transmission processor 51008 of FIG. 15 .

FIG. 36 is a flowchart of a method of receiving point cloud dataaccording to embodiments.

According to embodiments, a point cloud data reception method mayinclude a step 81001 of receiving encoded geometries, encodedattributes, and signaling information, a step 81002 of decoding thegeometries based on the signaling information, a step 81003 of decodingthe attributes based on the signaling information and thedecoded/reconstructed geometries, and a step 81004 of rendering pointcloud data restored based on the decoded geometries and the decodedattributes.

The step 81001 according to embodiments may be performed by the receiver10005 of FIG. 1 , the transmitting process 20002 or the decoding process20003 of FIG. 2 , the receiver 13000 or the reception processor 13001 ofFIG. 13 or the reception processor 61001 of FIG. 20 .

In the steps 81002 and 81003 according to embodiments, decoding may beperformed on the basis of a slice or a tile containing one or moreslices.

According to embodiments, the step 81002 may perform some or all of theoperations of the point cloud video decoder 10006 of FIG. 1 , thedecoding process 20003 of FIG. 2 , the point cloud video decoder of FIG.11 , the point cloud video decoder of FIG. 13 , the geometry decoder ofFIG. 20 or the geometry decoder of FIG. 21 .

According to embodiments, the step 81003 may perform some or all of theoperations of the point cloud video decoder 10006 of FIG. 1 , thedecoding process 20003 of FIG. 2 , the point cloud video decoder of FIG.11 , the point cloud video decoder of FIG. 13 , the attribute decoder ofFIG. 20 or the attribute decoder of FIG. 21 .

According to embodiments, the signaling information such as a sequenceparameter set, an attribute parameter set, a tile parameter set, or anattribute slice header may include information on the maximum number ofpredictors that is used for attribute prediction(lifting_max_num_direct_predictors), threshold information for enablingadaptive prediction of an attribute(lifting_adaptive_prediction_threshold), a base predictor selectionmethod (attribute_base_predictor_selection_type), an attributesimilarity check method (attribute_similarity_check_method_type), andthe like.

According to an embodiment, attribute_slice_data may include predictorindex information (predIndex) indicating a prediction mode of a point tobe decoded.

According to an embodiment, the step 81003 may predict an attributevalue of a point to be decoded based on the prediction mode 0 when thepredictor index information of the point is not signaled in theattribute slice data.

According to an embodiment, the step 81003 may predict an attributevalue of a point to be decoded based on a prediction mode that issignaled in the attribute slice data when the predictor indexinformation of the point is signaled in the attribute slice data.

According to an embodiment, the step 81003 may restore an attributevalue of the point by adding the predicted attribute value of the pointand the received residual attribute value of the point. In anembodiment, an attribute value of each point may be restored byperforming this process for each point.

According to an embodiment, the step 81003 may perform a zero-run lengthdecoding on the received residual attribute values, which is an inverseprocess of a zero-run length encoding of a transmitting side, prior torestore the attribute values when the received residual attribute valuesare zero-run length encoded.

In the step 81004 of rendering the point cloud data according to theembodiments, the point cloud data may be rendered according to variousrendering methods. For example, the points of the point cloud contentmay be rendered onto a vertex having a certain thickness, a cube of aspecific minimum size centered on the vertex position, or a circlecentered on the vertex position. All or part of the rendered point cloudcontent is provided to the user through a display (e.g. a VR/AR display,a general display, etc.).

The step 81004 according to the embodiments may be performed by therenderer 10007 of FIG. 1 , the rendering process 20004 of FIG. 2 , orthe renderer 13011 of FIG. 13 .

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments do not allow that a predictionmode for calculating a predicted attribute value through a weightedaverage is set as a prediction mode of the point when a maximumdifference between attribute values of neighbor points registered in thepredictor of the corresponding point is greater than or equal to apreset threshold. Accordingly, the present disclosure can reduce thebitstream size of the attribute information and enhance compressionefficiency of the attribute information.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may provide a good-qualitypoint cloud service.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may achieve various videocodec methods.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may provide universal pointcloud content such as a self-driving service (or an autonomous drivingservice).

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may perform space-adaptivepartition of point cloud data for independent encoding and decoding ofthe point cloud data, thereby improving parallel processing andproviding scalability.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may perform encoding anddecoding by spatially partitioning the point cloud data in units oftiles and/or slices, and signal necessary data therefore, therebyimproving encoding and decoding performance of the point cloud.

A point cloud data transmission method, a point cloud data transmissiondevice, a point cloud data reception method, and a point cloud datareception device according to embodiments may change a method ofselecting the most suitable predictor among predictor candidates inencoding attribute information, thereby reducing the bitstream size ofthe attribute information and enhancing compression efficiency of theattribute information.

When a maximum difference between attribute values of neighbor pointsregistered in the predictor of the corresponding point is greater thanor equal to a preset threshold, a point cloud data transmission method,a point cloud data transmission device, a point cloud data receptionmethod, and a point cloud data reception device according to embodimentsmay reduce the size of an attribute bitstream including residualattribute values by not allowing a prediction mode for calculating apredicted attribute value through a weighted average to be set as aprediction mode of the point. Thereby, compression efficiency of theattributes may be enhanced.

Each part, module, or unit described above may be a software, processor,or hardware part that executes successive procedures stored in a memory(or storage unit). Each of the steps described in the above embodimentsmay be performed by a processor, software, or hardware parts. Eachmodule/block/unit described in the above embodiments may operate as aprocessor, software, or hardware. In addition, the methods presented bythe embodiments may be executed as code. This code may be written on aprocessor readable storage medium and thus read by a processor providedby an apparatus.

In the specification, when a part “comprises” or “includes” an element,it means that the part further comprises or includes another elementunless otherwise mentioned. Also, the term “ . . . module(or unit)”disclosed in the specification means a unit for processing at least onefunction or operation, and may be implemented by hardware, software orcombination of hardware and software.

Although embodiments have been explained with reference to each of theaccompanying drawings for simplicity, it is possible to design newembodiments by merging the embodiments illustrated in the accompanyingdrawings. If a recording medium readable by a computer, in whichprograms for executing the embodiments mentioned in the foregoingdescription are recorded, is designed by those skilled in the art, itmay fall within the scope of the appended claims and their equivalents.

The apparatuses and methods may not be limited by the configurations andmethods of the embodiments described above. The embodiments describedabove may be configured by being selectively combined with one anotherentirely or in part to enable various modifications.

Although preferred embodiments have been described with reference to thedrawings, those skilled in the art will appreciate that variousmodifications and variations may be made in the embodiments withoutdeparting from the spirit or scope of the disclosure described in theappended claims. Such modifications are not to be understoodindividually from the technical idea or perspective of the embodiments.

Various elements of the apparatuses of the embodiments may beimplemented by hardware, software, firmware, or a combination thereof.Various elements in the embodiments may be implemented by a single chip,for example, a single hardware circuit. According to embodiments, thecomponents according to the embodiments may be implemented as separatechips, respectively. According to embodiments, at least one or more ofthe components of the apparatus according to the embodiments may includeone or more processors capable of executing one or more programs. Theone or more programs may perform any one or more of theoperations/methods according to the embodiments or include instructionsfor performing the same. Executable instructions for performing themethod/operations of the apparatus according to the embodiments may bestored in a non-transitory CRM or other computer program productsconfigured to be executed by one or more processors, or may be stored ina transitory CRM or other computer program products configured to beexecuted by one or more processors. In addition, the memory according tothe embodiments may be used as a concept covering not only volatilememories (e.g., RAM) but also nonvolatile memories, flash memories, andPROMs. In addition, it may also be implemented in the form of a carrierwave, such as transmission over the Internet. In addition, theprocessor-readable recording medium may be distributed to computersystems connected over a network such that the processor-readable codemay be stored and executed in a distributed fashion.

In this document, the term “/” and “,” should be interpreted asindicating “and/or.” For instance, the expression “A/B” may mean “Aand/or B.” Further, “A, B” may mean “A and/or B.” Further, “A/B/C” maymean “at least one of A, B, and/or C.” “A, B, C” may also mean “at leastone of A, B, and/or C.” Further, in the document, the term “or” shouldbe interpreted as “and/or.” For instance, the expression “A or B” maymean 1) only A, 2) only B, and/or 3) both A and B. In other words, theterm “or” in this document should be interpreted as “additionally oralternatively.”

Terms such as first and second may be used to describe various elementsof the embodiments. However, various components according to theembodiments should not be limited by the above terms. These terms areonly used to distinguish one element from another. For example, a firstuser input signal may be referred to as a second user input signal.Similarly, the second user input signal may be referred to as a firstuser input signal. Use of these terms should be construed as notdeparting from the scope of the various embodiments. The first userinput signal and the second user input signal are both user inputsignals, but do not mean the same user input signal unless contextclearly dictates otherwise. The terminology used to describe theembodiments is used for the purpose of describing particular embodimentsonly and is not intended to be limiting of the embodiments. As used inthe description of the embodiments and in the claims, the singular forms“a”, “an”, and “the” include plural referents unless the context clearlydictates otherwise. The expression “and/or” is used to include allpossible combinations of terms. The terms such as “includes” or “has”are intended to indicate existence of figures, numbers, steps, elements,and/or components and should be understood as not precluding possibilityof existence of additional existence of figures, numbers, steps,elements, and/or components.

As used herein, conditional expressions such as “if” and “when” are notlimited to an optional case and are intended to be interpreted, when aspecific condition is satisfied, to perform the related operation orinterpret the related definition according to the specific condition.Embodiments may include variations/modifications within the scope of theclaims and their equivalents. It will be apparent to those skilled inthe art that various modifications and variations can be made in thepresent invention without departing from the spirit and scope of theinvention. Thus, it is intended that the present invention cover themodifications and variations of this invention provided they come withinthe scope of the appended claims and their equivalents.

What is claimed is:
 1. A point cloud data transmission method by anapparatus, the method comprising: acquiring point cloud data; encodinggeometry information including positions of points in the point clouddata; encoding attribute information including attribute values ofpoints in the point cloud data based on the geometry information; andtransmitting the encoded geometry information, the encoded attributeinformation, and signaling information, wherein encoding the attributeinformation comprises: generating predictor candidates based on nearestneighbor points of a point to be encoded when a maximum difference valuebetween attribute values of the nearest neighbor points is equal orgreater than a threshold value, wherein each predictor candidatecorresponds to each prediction mode and wherein a predicted attributevalue of the point for the each prediction mode is set as an attributevalue of each one of the nearest neighbor points, obtaining a score ofthe each predictor candidate and setting a prediction mode correspondingto a predictor candidate that has a smallest score, wherein the score ofthe each predictor candidate is obtained based on each residualattribute value obtained by applying an input attribute value of thepoint to the predicted attribute value for the each prediction mode,quantizing a residual attribute value of the point based on the setprediction mode, and encoding the quantized residual attribute value ofthe point.
 2. The method of claim 1, wherein the quantized residualattribute value of the point is entropy-encoded.
 3. The method of claim1, wherein the each prediction mode is a prediction mode 1, a predictionmode 2, or a prediction mode 3, wherein the predicted attribute value ofthe point for the prediction mode 1 is set as an attribute value of afirst nearest neighbor point of the nearest neighbor points, wherein thepredicted attribute value of the point for the prediction mode 2 is setas an attribute value of a second nearest neighbor point of the nearestneighbor points, and wherein the predicted attribute value of the pointfor the prediction mode 3 is set as an attribute value of a thirdnearest neighbor point of the nearest neighbor points.
 4. The method ofclaim 1, further comprising: setting a prediction mode 0 when themaximum difference value between attribute values of the nearestneighbor points is less than the threshold value, wherein a predictedattribute value of the point for the prediction mode 0 is set as adistance based weighted average value of the nearest neighbor pointsquantizing a residual attribute value of the point based on the setprediction mode 0, and encoding the quantized residual attribute valueof the point.
 5. A point cloud data transmission apparatus, theapparatus comprising: an acquisition unit to acquire point cloud data; ageometry encoder to encode geometry information including positions ofpoints in the point cloud data; an attribute encoder to encode attributeinformation including attribute values of points in the point cloud databased on the geometry information; and a transmitter to transmit theencoded geometry information, the encoded attribute information, andsignaling information, wherein the attribute encoder: generatespredictor candidates based on nearest neighbor points of a point to beencoded when a maximum difference value between attribute values of thenearest neighbor points is equal or greater than a threshold value,wherein each predictor candidate corresponds to each prediction mode andwherein a predicted attribute value of the point for the each predictionmode is set as an attribute value of each one of the nearest neighborpoints, obtains a score of each predictor candidate and sets aprediction mode corresponding to a predictor candidate that has asmallest score, wherein the score of the each predictor candidate isobtained based on each residual attribute value obtained by applying aninput attribute value of the point to the predicted attribute value forthe each prediction mode, and quantizes a residual attribute value ofthe point based on the set prediction mode and encodes the quantizedresidual attribute value of the point.
 6. The apparatus of claim 5,wherein the quantized residual attribute value of the point isentropy-encoded.
 7. The apparatus of claim 5, wherein the eachprediction mode is a prediction mode 1, a prediction mode 2, or aprediction mode 3, wherein the predicted attribute value of the pointfor the prediction mode 1 is set as an attribute value of a firstnearest neighbor point of the nearest neighbor points, wherein thepredicted attribute value of the point for the prediction mode 2 is setas an attribute value of a second nearest neighbor point of the nearestneighbor points, and wherein the predicted attribute value of the pointfor the prediction mode 3 is set as an attribute value of a thirdnearest neighbor point of the nearest neighbor points.
 8. The apparatusof claim 5, wherein the attribute encoder: sets a prediction mode 0 whenthe maximum difference value between the attribute values of the nearestneighbor points is less than the threshold value, wherein a predictedattribute value of the point for the prediction mode 0 is set as adistance based weighted average value of the nearest neighbor points,quantizes a residual attribute value of the point based on the setprediction mode 0, and encodes the quantized residual attribute value ofthe point.
 9. A point cloud data reception apparatus, the apparatuscomprising: a receiver to receive geometry information, attributeinformation, and signaling information; a geometry decoder to decode thegeometry information based on the signaling information and restorepositions of points; an attribute decoder to decode the attributeinformation based on the signaling information and the geometryinformation and restore attribute values of the points; a renderer torender point cloud data restored based on the positions and attributevalues of the points, wherein the attribute decoder: obtains aprediction mode of a point to be decoded, wherein, when a maximumdifference value between attribute values of nearest neighbor points ofthe point is equal or greater than a threshold value, a predictedattribute value of the point is set as an attribute value of one of thenearest neighbor points according to the obtained prediction mode,performs inverse quantization on a residual attribute value of the pointin the attribute information, and restores an attribute value of thepoint based on the predicted attribute value of the point and theinverse-quantized residual attribute value of the point in the attributeinformation.
 10. The apparatus of claim 9, wherein the obtainedprediction mode of the point is one of a prediction mode 1, a predictionmode 2 or a prediction mode 3, wherein the predicted attribute value ofthe point for the prediction mode 1 is set as an attribute value of afirst nearest neighbor point of the nearest neighbor points, wherein thepredicted attribute value of the point for the prediction mode 2 is setas an attribute value of a second nearest neighbor point of the nearestneighbor points, and wherein the predicted attribute value of the pointfor the prediction mode 3 is set as an attribute value of a thirdnearest neighbor point of the nearest neighbor points.
 11. The apparatusof claim 9, wherein the obtained prediction mode of the point is aprediction mode 0 when the maximum difference value between theattribute values of the nearest neighbor points is less than thethreshold value, and wherein a predicted attribute value of the pointfor the prediction mode 0 is set as a distance based weighted averagevalue of the nearest neighbor points.
 12. A point cloud data receptionmethod by an apparatus, the method comprising: receiving geometryinformation, attribute information, and signaling information; decodingthe geometry information based on the signaling information and restorepositions of points; decoding the attribute information based on thesignaling information and the geometry information and restore attributevalues of the points; rendering point cloud data restored based on thepositions and attribute values of the points, wherein decoding theattribute information comprises: obtaining a prediction mode of a pointto be decoded, wherein, when a maximum difference value betweenattribute values of nearest neighbor points of the point is equal orgreater than a threshold value, a predicted attribute value of the pointis set as an attribute value of one of the nearest neighbor pointsaccording to the obtained prediction mode, performing inversequantization on a residual attribute value of the point in the attributeinformation, and restoring an attribute value of the point based on thepredicted attribute value of the point and the inverse-quantizedresidual attribute value of the point in the attribute information. 13.The method of claim 12, wherein the obtained prediction mode of thepoint is one of a prediction mode 1, a prediction mode 2 or a predictionmode 3, wherein the predicted attribute value of the point for theprediction mode 1 is set as an attribute value of a first nearestneighbor point of the nearest neighbor points, wherein the predictedattribute value of the point for the prediction mode 2 is set as anattribute value of a second nearest neighbor point of the nearestneighbor points, and wherein the predicted attribute value of the pointfor the prediction mode 3 is set as an attribute value of a thirdnearest neighbor point of the nearest neighbor points.
 14. The method ofclaim 12, wherein the obtained prediction mode of the point is aprediction mode 0 when the maximum difference value between theattribute values of the nearest neighbor points is less than thethreshold value, and wherein a predicted attribute value of the pointfor the prediction mode 0 is set as a distance based weighted averagevalue of the nearest neighbor points.